62 results on '"Chen, Changhao"'
Search Results
2. Matching Query Image Against Selected NeRF Feature for Efficient and Scalable Localization
- Author
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Zhou, Huaiji, Wang, Bing, and Chen, Changhao
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Neural implicit representations such as NeRF have revolutionized 3D scene representation with photo-realistic quality. However, existing methods for visual localization within NeRF representations suffer from inefficiency and scalability issues, particularly in large-scale environments. This work proposes MatLoc-NeRF, a novel matching-based localization framework using selected NeRF features. It addresses efficiency by employing a learnable feature selection mechanism that identifies informative NeRF features for matching with query images. This eliminates the need for all NeRF features or additional descriptors, leading to faster and more accurate pose estimation. To tackle large-scale scenes, MatLoc-NeRF utilizes a pose-aware scene partitioning strategy. It ensures that only the most relevant NeRF sub-block generates key features for a specific pose. Additionally, scene segmentation and a place predictor provide fast coarse initial pose estimation. Evaluations on public large-scale datasets demonstrate that MatLoc-NeRF achieves superior efficiency and accuracy compared to existing NeRF-based localization methods., Comment: 12 pages, 2 figures
- Published
- 2024
3. ConcertoRL: An Innovative Time-Interleaved Reinforcement Learning Approach for Enhanced Control in Direct-Drive Tandem-Wing Vehicles
- Author
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Zhang, Minghao, Song, Bifeng, Chen, Changhao, and Lang, Xinyu
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Robotics ,68T40 ,I.2.9 - Abstract
In control problems for insect-scale direct-drive experimental platforms under tandem wing influence, the primary challenge facing existing reinforcement learning models is their limited safety in the exploration process and the stability of the continuous training process. We introduce the ConcertoRL algorithm to enhance control precision and stabilize the online training process, which consists of two main innovations: a time-interleaved mechanism to interweave classical controllers with reinforcement learning-based controllers aiming to improve control precision in the initial stages, a policy composer organizes the experience gained from previous learning to ensure the stability of the online training process. This paper conducts a series of experiments. First, experiments incorporating the time-interleaved mechanism demonstrate a substantial performance boost of approximately 70% over scenarios without reinforcement learning enhancements and a 50% increase in efficiency compared to reference controllers with doubled control frequencies. These results highlight the algorithm's ability to create a synergistic effect that exceeds the sum of its parts., Comment: 48 pages, 35 figures
- Published
- 2024
4. EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization
- Author
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Xiao, Zhendong, Chen, Changhao, Yang, Shan, and Wei, Wu
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Camera relocalization is pivotal in computer vision, with applications in AR, drones, robotics, and autonomous driving. It estimates 3D camera position and orientation (6-DoF) from images. Unlike traditional methods like SLAM, recent strides use deep learning for direct end-to-end pose estimation. We propose EffLoc, a novel efficient Vision Transformer for single-image camera relocalization. EffLoc's hierarchical layout, memory-bound self-attention, and feed-forward layers boost memory efficiency and inter-channel communication. Our introduced sequential group attention (SGA) module enhances computational efficiency by diversifying input features, reducing redundancy, and expanding model capacity. EffLoc excels in efficiency and accuracy, outperforming prior methods, such as AtLoc and MapNet. It thrives on large-scale outdoor car-driving scenario, ensuring simplicity, end-to-end trainability, and eliminating handcrafted loss functions., Comment: 8 pages, 6 figures, ICRA 2024 accepted
- Published
- 2024
5. DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing
- Author
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Qu, Hao, Zhang, Lilian, Mao, Jun, Tie, Junbo, He, Xiaofeng, Hu, Xiaoping, Shi, Yifei, and Chen, Changhao
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features. Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks, enhancing their adaptability to diverse environments. Additionally, we introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It begins with a direct method to approximate the relative pose between consecutive frames, followed by a feature matching method for refined pose estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection. This module dynamically identifies loop nodes within a sequence, ensuring accurate and efficient localization. Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM. These results underscore the efficacy and robustness of our DK-SLAM in varied and challenging real-world environments., Comment: In submission
- Published
- 2024
6. ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization
- Author
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Chen, Zongyang, Pan, Xianfei, and Chen, Changhao
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Accurately and reliably positioning pedestrians in satellite-denied conditions remains a significant challenge. Pedestrian dead reckoning (PDR) is commonly employed to estimate pedestrian location using low-cost inertial sensor. However, PDR is susceptible to drift due to sensor noise, incorrect step detection, and inaccurate stride length estimation. This work proposes ReLoc-PDR, a fusion framework combining PDR and visual relocalization using graph optimization. ReLoc-PDR leverages time-correlated visual observations and learned descriptors to achieve robust positioning in visually-degraded environments. A graph optimization-based fusion mechanism with the Tukey kernel effectively corrects cumulative errors and mitigates the impact of abnormal visual observations. Real-world experiments demonstrate that our ReLoc-PDR surpasses representative methods in accuracy and robustness, achieving accurte and robust pedestrian positioning results using only a smartphone in challenging environments such as less-textured corridors and dark nighttime scenarios., Comment: 11 pages, 14 figures
- Published
- 2023
7. Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey
- Author
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Jia, Zhihao, Wang, Bing, and Chen, Changhao
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
Neural rendering has garnered substantial attention owing to its capacity for creating realistic 3D scenes. However, its applicability to extensive scenes remains challenging, with limitations in effectiveness. In this work, we propose the Drone-NeRF framework to enhance the efficient reconstruction of unbounded large-scale scenes suited for drone oblique photography using Neural Radiance Fields (NeRF). Our approach involves dividing the scene into uniform sub-blocks based on camera position and depth visibility. Sub-scenes are trained in parallel using NeRF, then merged for a complete scene. We refine the model by optimizing camera poses and guiding NeRF with a uniform sampler. Integrating chosen samples enhances accuracy. A hash-coded fusion MLP accelerates density representation, yielding RGB and Depth outputs. Our framework accounts for sub-scene constraints, reduces parallel-training noise, handles shadow occlusion, and merges sub-regions for a polished rendering result. This Drone-NeRF framework demonstrates promising capabilities in addressing challenges related to scene complexity, rendering efficiency, and accuracy in drone-obtained imagery., Comment: 15 pages, 7 figures, in submission
- Published
- 2023
8. Deep Learning for Visual Localization and Mapping: A Survey
- Author
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Chen, Changhao, Wang, Bing, Lu, Chris Xiaoxuan, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia. Instead of creating hand-designed algorithms based on physical models or geometric theories, deep learning solutions provide an alternative to solve the problem in a data-driven way. Benefiting from the ever-increasing volumes of data and computational power on devices, these learning methods are fast evolving into a new area that shows potentials to track self-motion and estimate environmental model accurately and robustly for mobile agents. In this work, we provide a comprehensive survey, and propose a taxonomy for the localization and mapping methods using deep learning. This survey aims to discuss two basic questions: whether deep learning is promising to localization and mapping; how deep learning should be applied to solve this problem. To this end, a series of localization and mapping topics are investigated, from the learning based visual odometry, global relocalization, to mapping, and simultaneous localization and mapping (SLAM). It is our hope that this survey organically weaves together the recent works in this vein from robotics, computer vision and machine learning communities, and serves as a guideline for future researchers to apply deep learning to tackle the problem of visual localization and mapping., Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems. This is an updated version of arXiv:2006.12567
- Published
- 2023
9. The Structure and Morphology of Galaxies during the Epoch of Reionization Revealed by JWST
- Author
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Sun, Wen, Ho, Luis C., Zhuang, Ming-Yang, Ma, Chao, Chen, Changhao, and Li, Ruancun
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Astrophysics - Astrophysics of Galaxies - Abstract
We analyze 347 galaxies at redshift $4
- Published
- 2023
10. Local mean value estimates for Weyl sums
- Author
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Brandes, Julia, Chen, Changhao, and Shparlinski, Igor E.
- Subjects
Mathematics - Number Theory ,Mathematics - Classical Analysis and ODEs ,Primary: 11L15, Secondary: 11L07, 11D45 - Abstract
We obtain new estimates - both upper and lower bounds - on the mean values of the Weyl sums over a small box inside of the unit torus. In particular, we refine recent conjectures of C. Demeter and B. Langowski (2022), and improve some of their results., Comment: 36 pages,3 figures
- Published
- 2023
11. Deep Learning for Inertial Positioning: A Survey
- Author
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Chen, Changhao and Pan, Xianfei
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field., Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
- Published
- 2023
12. SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery
- Author
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Qu, Hao, Zhang, Lilian, Hu, Xiaoping, He, Xiaofeng, Pan, Xianfei, and Chen, Changhao
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
Accurately perceiving location and scene is crucial for autonomous driving and mobile robots. Recent advances in deep learning have made it possible to learn egomotion and depth from monocular images in a self-supervised manner, without requiring highly precise labels to train the networks. However, monocular vision methods suffer from a limitation known as scale-ambiguity, which restricts their application when absolute-scale is necessary. To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images. In particular, we introduce a novel coarse-to-fine training strategy that enables the metric scale to be recovered in a two-stage process. Furthermore, SelfOdom is flexible and can incorporate inertial data with images, which improves its robustness in challenging scenarios, using an attention-based fusion module. Our model excels in both normal and challenging lighting conditions, including difficult night scenes. Extensive experiments on public datasets have demonstrated that SelfOdom outperforms representative traditional and learning-based VO and VIO models., Comment: 14 pages, 8 figures, in submission
- Published
- 2022
13. Orthogonal projections of planar sets in countable directions
- Author
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Chen, Changhao
- Subjects
Mathematics - Classical Analysis and ODEs - Abstract
Given $0
0$. For any $t/2\le s - Published
- 2022
14. EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention
- Author
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Tu, Zheming, Chen, Changhao, Pan, Xianfei, Liu, Ruochen, Cui, Jiarui, and Mao, Jun
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
Accurate and robust localization is a fundamental need for mobile agents. Visual-inertial odometry (VIO) algorithms exploit the information from camera and inertial sensors to estimate position and translation. Recent deep learning based VIO models attract attentions as they provide pose information in a data-driven way, without the need of designing hand-crafted algorithms. Existing learning based VIO models rely on recurrent models to fuse multimodal data and process sensor signal, which are hard to train and not efficient enough. We propose a novel learning based VIO framework with external memory attention that effectively and efficiently combines visual and inertial features for states estimation. Our proposed model is able to estimate pose accurately and robustly, even in challenging scenarios, e.g., on overcast days and water-filled ground , which are difficult for traditional VIO algorithms to extract visual features. Experiments validate that it outperforms both traditional and learning based VIO baselines in different scenes., Comment: Accepted by IEEE Sensors Journal
- Published
- 2022
15. DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
- Author
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Zhou, Kaichen, Hong, Lanqing, Chen, Changhao, Xu, Hang, Ye, Chaoqiang, Hu, Qingyong, and Li, Zhenguo
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully exploit the 3D point-wise geometric correspondences, nor effectively tackle the ambiguities in the photometric warping caused by occlusions or illumination inconsistency. To address these problems, this work proposes Density Volume Construction Network (DevNet), a novel self-supervised monocular depth learning framework, that can consider 3D spatial information, and exploit stronger geometric constraints among adjacent camera frustums. Instead of directly regressing the pixel value from a single image, our DevNet divides the camera frustum into multiple parallel planes and predicts the pointwise occlusion probability density on each plane. The final depth map is generated by integrating the density along corresponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-2015 outdoor dataset and NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth estimation. Code is available at https://github.com/gitkaichenzhou/DevNet., Comment: Accepted by European Conference on Computer Vision 2022 (ECCV2022)
- Published
- 2022
16. Large Weyl sums and Hausdorff dimension
- Author
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Baker, Roger C., Chen, Changhao, and Shparlinski, Igor E.
- Subjects
Mathematics - Number Theory ,Mathematics - Classical Analysis and ODEs ,11L15, 11K55 - Abstract
We obtain the exact value of the Hausdorff dimension of the set of coefficients of Gauss sums which for a given $\alpha \in (1/2,1)$ achieve the order at least $N^{\alpha}$ for infinitely many sum lengths $N$. For Weyl sums with polynomials of degree $d\ge 3$ we obtain a new upper bound on the Hausdorff dimension of the set of polynomial coefficients corresponding to large values of Weyl sums. Our methods also work for monomial sums, match the previously known lower bounds, just giving exact value for the corresponding Hausdorff dimension when $\alpha$ is close to $1$. We also obtain a nearly tight bound in a similar question with arbitrary integer sequences of polynomial growth., Comment: 51 pages, 0 figures
- Published
- 2021
17. Bounds on the Norms of Maximal Operators on Weyl Sums
- Author
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Baker, Roger C., Chen, Changhao, and Shparlinski, Igor E.
- Subjects
Mathematics - Number Theory - Abstract
We obtain new estimates on the maximal operator applied to the Weyl sums. We also consider the quadratic case (that is, Gauss sums) in more details. In wide ranges of parameters our estimates are optimal and match lower bounds. Our approach is based on a combination of ideas of Baker (2021) and Chen and Shparlinski (2020).
- Published
- 2021
18. P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
- Author
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Wang, Bing, Chen, Changhao, Cui, Zhaopeng, Qin, Jie, Lu, Chris Xiaoxuan, Yu, Zhengdi, Zhao, Peijun, Dong, Zhen, Zhu, Fan, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D local feature descriptors and detectors having been proposed, the derivation of a shared descriptor and joint keypoint detector that directly matches pixels and points remains under-explored by the community. This work takes the initiative to establish fine-grained correspondences between 2D images and 3D point clouds. In order to directly match pixels and points, a dual fully convolutional framework is presented that maps 2D and 3D inputs into a shared latent representation space to simultaneously describe and detect keypoints. Furthermore, an ultra-wide reception mechanism in combination with a novel loss function are designed to mitigate the intrinsic information variations between pixel and point local regions. Extensive experimental results demonstrate that our framework shows competitive performance in fine-grained matching between images and point clouds and achieves state-of-the-art results for the task of indoor visual localization. Our source code will be available at [no-name-for-blind-review]., Comment: ICCV 2021
- Published
- 2021
19. Metric theory of Weyl sums
- Author
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Chen, Changhao, Kerr, Bryce, Maynard, James, and Shparlinski, Igor
- Subjects
Mathematics - Number Theory - Abstract
We prove that there exist positive constants $C$ and $c$ such that for any integer $d \ge 2$ the set of ${\mathbf x}\in [0,1)^d$ satisfying $$ cN^{1/2}\le \left|\sum^N_{n=1}\exp\left (2 \pi i \left (x_1n+\ldots+x_d n^d\right)\right) \right|\le C N^{1/2}$$ for infinitely many natural numbers $N$ is of full Lebesque measure. This substantially improves the previous results where similar sets have been measured in terms of the Hausdorff dimension. We also obtain similar bounds for exponential sums with monomials $xn^d$ when $d\neq 4$. Finally, we obtain lower bounds for the Hausdorff dimension of large values of general exponential polynomials., Comment: arXiv admin note: text overlap with arXiv:2004.02539
- Published
- 2020
20. A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
- Author
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Chen, Changhao, Wang, Bing, Lu, Chris Xiaoxuan, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Robotics ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or geometric theories, deep learning based solutions provide an alternative to solve the problem in a data-driven way. Benefiting from ever-increasing volumes of data and computational power, these methods are fast evolving into a new area that offers accurate and robust systems to track motion and estimate scenes and their structure for real-world applications. In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. We also discuss the limitations of current models, and indicate possible future directions. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping (SLAM). We revisit the problem of perceiving self-motion and scene understanding with on-board sensors, and show how to solve it by integrating these modules into a prospective spatial machine intelligence system (SMIS). It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities, and serve as a guide for future researchers to apply deep learning to tackle localization and mapping problems., Comment: 26 pages, 10 figures. Project website: https://github.com/changhao-chen/deep-learning-localization-mapping
- Published
- 2020
21. milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion
- Author
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Lu, Chris Xiaoxuan, Saputra, Muhamad Risqi U., Zhao, Peijun, Almalioglu, Yasin, de Gusmao, Pedro P. B., Chen, Changhao, Sun, Ke, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Robotics - Abstract
Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by optical techniques e.g., visual-inertial odometry, these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose milliEgo, a novel deep-learning approach to robust egomotion estimation which exploits the capabilities of low-cost mmWave radar. Although mmWave radar has a fundamental advantage over monocular cameras of being metric i.e., providing absolute scale or depth, current single chip solutions have limited and sparse imaging resolution, making existing point-cloud registration techniques brittle. We propose a new architecture that is optimized for solving this challenging pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensors we introduce a mixed attention approach to deep fusion. Through extensive experiments, we demonstrate our proposed system is able to achieve 1.3% 3D error drift and generalizes well to unseen environments. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications., Comment: Appear at the ACM Conference on Embedded Networked Sensor Systems (SenSys 2020)
- Published
- 2020
22. Self-similar sets with super-exponential close cylinders
- Author
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Chen, Changhao
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory ,28A80, 11J70 - Abstract
S. Baker (2019), B. B\'ar\'any and A. K\"{a}enm\"{a}ki (2019) independently showed that there exist iterated function systems without exact overlaps and there are super-exponentially close cylinders at all small levels. We adapt the method of S. Baker and obtain further examples of this type. We prove that for any algebraic number $\beta\ge 2$ there exist real numbers $s, t$ such that the iterated function system $$ \left \{\frac{x}{\beta}, \frac{x+1}{\beta}, \frac{x+s}{\beta}, \frac{x+t}{\beta}\right \} $$ satisfies the above property., Comment: 15 pages
- Published
- 2020
23. Metric theory of lower bounds on Weyl sums
- Author
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Chen, Changhao, Kerr, Bryce, and Shparlinski, Igor
- Subjects
Mathematics - Number Theory ,Mathematics - Functional Analysis - Abstract
We prove that the Hausdorff dimension of the set $\mathbf{x}\in [0,1)^d$, such that $$ \left|\sum_{n=1}^N \exp\left(2 \pi i\left(x_1n+\ldots+x_d n^d\right)\right) \right|\ge c N^{1/2} $$ holds for infinitely many natural numbers $N$, is at least $d-1/2d$ for $d \ge 3$ and at least $3/2$ for $d=2$, where $c$ is a constant depending only on $d$. This improves the previous lower bound of the first and third authors for $d\ge 3$. We also obtain similar bounds for the Hausdorff dimension of the set of large sums with monomials $xn^d$., Comment: All results of this preprint are now included, together with several other results, in 2011.09306 - "Metric theory of Weyl sums", by C. Chen, B. Kerr, J.Maynard, I. Shparlinski
- Published
- 2020
24. VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization
- Author
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Zhou, Kaichen, Chen, Changhao, Wang, Bing, Saputra, Muhamad Risqi U., Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Recent learning-based approaches have achieved impressive results in the field of single-shot camera localization. However, how best to fuse multiple modalities (e.g., image and depth) and to deal with degraded or missing input are less well studied. In particular, we note that previous approaches towards deep fusion do not perform significantly better than models employing a single modality. We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality. To address this, we propose an end-to-end framework, termed VMLoc, to fuse different sensor inputs into a common latent space through a variational Product-of-Experts (PoE) followed by attention-based fusion. Unlike previous multimodal variational works directly adapting the objective function of vanilla variational auto-encoder, we show how camera localization can be accurately estimated through an unbiased objective function based on importance weighting. Our model is extensively evaluated on RGB-D datasets and the results prove the efficacy of our model. The source code is available at https://github.com/kaichen-z/VMLoc.
- Published
- 2020
25. Hybrid bounds on two-parametric family Weyl sums along smooth curves
- Author
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Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory ,11L15, 35Q35 - Abstract
We obtain a new bound on Weyl sums with degree $k\ge 2$ polynomials of the form $(\tau x+c) \omega(n)+xn$, $n=1, 2, \ldots$, with fixed $\omega(T) \in \mathbb{Z}[T]$ and $\tau \in \mathbb{R}$, which holds for almost all $c\in [0,1)$ and all $x\in [0,1)$. We improve and generalise some recent results of M.~B.~Erdogan and G.~Shakan (2019), whose work also shows links between this question and some classical partial differential equations. We extend this to more general settings of families of polynomials $xn+y \omega(n)$ for all $(x,y)\in [0,1)^2$ with $f(x,y)=z$ for a set of $z \in [0,1)$ of full Lebesgue measure, provided that $f$ is some H\"older function., Comment: 18 pages
- Published
- 2020
26. PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
- Author
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Wang, Wei, Wang, Bing, Zhao, Peijun, Chen, Changhao, Clark, Ronald, Yang, Bo, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Robotics - Abstract
In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360{\deg} LiDAR input frames.Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposedmethod can achieve accurate relocalization performance., Comment: To appear in IEEE Sensors Journal 2021
- Published
- 2020
27. Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference
- Author
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Chen, Changhao, Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Wei, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Robotics ,Computer Science - Machine Learning - Abstract
Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices., Comment: Accepted to IEEE Internet of Things Journal
- Published
- 2020
28. Learning Selective Sensor Fusion for States Estimation
- Author
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Chen, Changhao, Rosa, Stefano, Lu, Chris Xiaoxuan, Wang, Bing, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Robotics - Abstract
Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g. locations and orientations. Although deep learning approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion - a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module which can be applied to useful pairs of sensor modalities such as monocular images and inertial measurements, depth images and LIDAR point clouds. Our model is a uniform framework that is not restricted to specific modality or task. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and estimate trajectory both at scale and global pose. In particular, we propose two fusion modules - a deterministic soft fusion and a stochastic hard fusion, and offer a comprehensive study of the new strategies compared to trivial direct fusion. We extensively evaluate all fusion strategies in both public datasets and on progressively degraded datasets that present synthetic occlusions, noisy and missing data and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself, provides insights into the operation of the various models., Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS). arXiv admin note: text overlap with arXiv:1903.01534
- Published
- 2019
29. Restricted mean value theorems and metric theory of restricted Weyl sums
- Author
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Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory ,11L07, 11L15, 28A78 - Abstract
We study an apparently new question about the behaviour of Weyl sums on a subset $\mathcal{X}\subseteq [0,1)^d$ with a natural measure $\mu$ on $\mathcal{X}$. For certain measure spaces $(\mathcal{X}, \mu)$ we obtain non-trivial bounds for the mean values of the Weyl sums, and for $\mu$-almost all points of $\mathcal{X}$ the Weyl sums satisfy the square root cancellation law. Moreover we characterise the size of the exceptional sets in terms of Hausdorff dimension. Finally, we derive variants of the Vinogradov mean value theorem averaging over measure spaces $(\mathcal{X}, \mu)$. We obtain general results, which we refine for some special spaces $\mathcal{X}$ such as spheres, moment curves and line segments., Comment: 37 pages
- Published
- 2019
30. See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
- Author
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Lu, Chris Xiaoxuan, Rosa, Stefano, Zhao, Peijun, Wang, Bing, Chen, Changhao, Stankovic, John A., Trigoni, Niki, and Markham, Andrew
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy around 90%, whilst operating through dense smoke., Comment: 14 pages, appear at the ACM Conference on Mobile Systems, Applications, and Services (MobiSys), 2020
- Published
- 2019
31. On a Hybrid Version of the Vinogradov Mean Value Theorem
- Author
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Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory - Abstract
Given a family $\varphi= (\varphi_1, \ldots, \varphi_d)\in \mathbb{Z}[T]^d$ of $d$ distinct nonconstant polynomials, a positive integer $k\le d$ and a real positive parameter $\rho$, we consider the mean value $$ M_{k, \rho} (\varphi, N) = \int_{\mathbf{x} \in [0,1]^k} \sup_{\mathbf{y} \in [0,1]^{d-k}} \left| S_{\varphi}(\mathbf{x}, \mathbf{y}; N) \right|^\rho d\mathbf{x} $$ of exponential sums $$ S_{\varphi}( \mathbf{x}, \mathbf{y}; N) = \sum_{n=1}^{N} \exp\left(2 \pi i \left(\sum_{j=1}^k x_j \varphi_j(n)+ \sum_{j=1}^{d-k}y_j\varphi_{k+j}(n)\right)\right), $$ where $\mathbf{x} = (x_1, \ldots, x_k)$ and $\mathbf{y} =(y_1, \ldots, y_{d-k})$. The case of polynomials $\varphi_i(T) = T^i$, $i =1, \ldots, d$ and $k=d$ corresponds to the classical Vinaogradov mean value theorem. Here motivated by recent works of Wooley (2015) and the authors (2019) on bounds on $\sup_{\mathbf{y} \in [0,1]^{d-k}} \left| S_{\varphi}( \mathbf{x}, \mathbf{y}; N) \right|$ for almost all $\mathbf{x} \in [0,1]^k$, we obtain nontrivial bounds on $M_{k, \rho} (\varphi, N)$., Comment: 16 pages. arXiv admin note: text overlap with arXiv:1903.07330
- Published
- 2019
32. DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
- Author
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Wang, Wei, Saputra, Muhamad Risqi U., Zhao, Peijun, Gusmao, Pedro, Yang, Bo, Chen, Changhao, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images. These learning-based approaches have led to more accurate and robust VO systems. However, they have not been well applied to point cloud data yet. In this work, we investigate how to exploit deep learning to estimate point cloud odometry (PCO), which may serve as a critical component in point cloud-based downstream tasks or learning-based systems. Specifically, we propose a novel end-to-end deep parallel neural network called DeepPCO, which can estimate the 6-DOF poses using consecutive point clouds. It consists of two parallel sub-networks to estimate 3-D translation and orientation respectively rather than a single neural network. We validate our approach on KITTI Visual Odometry/SLAM benchmark dataset with different baselines. Experiments demonstrate that the proposed approach achieves good performance in terms of pose accuracy., Comment: To appear in IROS 2019
- Published
- 2019
33. On uniform distribution of $\alpha\beta$-orbits
- Author
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Chen, Changhao, Wang, Xiaohua, and Wen, Shengyou
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory ,28A80, 11L03, 54E52 - Abstract
Let $\alpha, \beta \in (0,1)$ such that at least one of them is irrational. We take a random walk on the real line such that the choice of $\alpha$ and $\beta$ has equal probability $1/2$. We prove that almost surely the $\alpha\beta$-orbit is uniformly distributed module one, and the exponential sums along its orbit has the square root cancellation. We also show that the exceptional set in the probability space, which does not have the property of uniform distribution modulo one, is large in the terms of topology and Hausdorff dimension., Comment: 17 pages
- Published
- 2019
34. DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
- Author
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Saputra, Muhamad Risqi U., de Gusmao, Pedro P. B., Lu, Chris Xiaoxuan, Almalioglu, Yasin, Rosa, Stefano, Chen, Changhao, Wahlström, Johan, Wang, Wei, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Robotics - Abstract
Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model., Comment: Accepted to IEEE Robotics and Automation Letters (RAL)
- Published
- 2019
35. AtLoc: Attention Guided Camera Localization
- Author
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Wang, Bing, Chen, Changhao, Lu, Chris Xiaoxuan, Zhao, Peijun, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance. The source code is avaliable at https://github.com/BingCS/AtLoc.
- Published
- 2019
36. Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
- Author
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Lu, Chris Xiaoxuan, Kan, Xuan, Du, Bowen, Chen, Changhao, Wen, Hongkai, Markham, Andrew, Trigoni, Niki, and Stankovic, John
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture ,Statistics - Machine Learning - Abstract
Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak.We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort., Comment: 11 pages, accepted in the Web Conference (WWW'2019)
- Published
- 2019
- Full Text
- View/download PDF
37. DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
- Author
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Chen, Changhao, Lu, Chris Xiaoxuan, Wang, Bing, Trigoni, Niki, and Markham, Andrew
- Subjects
Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics ,Statistics - Machine Learning - Abstract
Dynamical models estimate and predict the temporal evolution of physical systems. State Space Models (SSMs) in particular represent the system dynamics with many desirable properties, such as being able to model uncertainty in both the model and measurements, and optimal (in the Bayesian sense) recursive formulations e.g. the Kalman Filter. However, they require significant domain knowledge to derive the parametric form and considerable hand-tuning to correctly set all the parameters. Data driven techniques e.g. Recurrent Neural Networks have emerged as compelling alternatives to SSMs with wide success across a number of challenging tasks, in part due to their ability to extract relevant features from rich inputs. They however lack interpretability and robustness to unseen conditions. In this work, we present DynaNet, a hybrid deep learning and time-varying state-space model which can be trained end-to-end. Our neural Kalman dynamical model allows us to exploit the relative merits of each approach. We demonstrate state-of-the-art estimation and prediction on a number of physically challenging tasks, including visual odometry, sensor fusion for visual-inertial navigation and pendulum control. In addition we show how DynaNet can indicate failures through investigation of properties such as the rate of innovation (Kalman Gain)., Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- Published
- 2019
38. Small values of Weyl sums
- Author
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Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Number Theory ,Mathematics - Classical Analysis and ODEs ,11J83, 11K38, 11L15 - Abstract
We prove that the set of $(x_1, \ldots, x_d)\in [0,1)^d$, such that $$ \underline{\lim}_{N\to \infty}\left| \sum_{n=1}^N\exp(2 \pi i (x_1n+\ldots + x_dn^d)) \right| =0, $$ contains a dense $\mathcal{G}_\delta$ set in $[0,1)^d$ and has a positive Hausdorff dimension. Similar statements are also established for the generalised Gaussian sums $$ \sum_{n=1}^N\exp(2\pi i x n^d), \qquad x \in [0,1). $$, Comment: 22 pages
- Published
- 2019
39. Hausdorff dimension of the large values of Weyl sums
- Author
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Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory ,11L15, 28A78, 28A80 - Abstract
The authors have recently obtained a lower bound of the Hausdorff dimension of the sets of vectors $(x_1, \ldots, x_d)\in [0,1)^d$ with large Weyl sums, namely of vectors for which $$ \left| \sum_{n=1}^{N}\exp(2\pi i (x_1 n+\ldots +x_d n^{d})) \right| \ge N^{\alpha} $$ for infinitely many integers $N \ge 1$. Here we obtain an upper bound for the Hausdorff dimension of these exceptional sets., Comment: 10 pages
- Published
- 2019
40. New bounds of Weyl sums
- Author
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Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Number Theory ,11K38, 11L15 - Abstract
We augment the method of Wooley (2015) by some new ideas and in a series of results, improve his metric bounds on the Weyl sums and the discrepancy of fractional parts of real polynomials with partially prescribed coefficients. We also extend these results and ideas to principally new and very general settings of arbitrary orthogonal projections of the vectors of the coefficients $(u_1, \ldots , u_d)$ onto a lower dimensional subspace. This new point of view has an additional advantage of yielding an upper bound on the Hausdorff dimension of sets of large Weyl sums. Among other technical innovations, we also introduce a ``self-improving'' approach, which leads an infinite series of monotonically decreasing bound, converging to our final result., Comment: 37 pages, To appear in Int. Math. Res. Notices
- Published
- 2019
41. Selective Sensor Fusion for Neural Visual-Inertial Odometry
- Author
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Chen, Changhao, Rosa, Stefano, Miao, Yishu, Lu, Chris Xiaoxuan, Wu, Wei, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Computer Science - Robotics - Abstract
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data. We propose a novel end-to-end selective sensor fusion framework for monocular VIO, which fuses monocular images and inertial measurements in order to estimate the trajectory whilst improving robustness to real-life issues, such as missing and corrupted data or bad sensor synchronization. In particular, we propose two fusion modalities based on different masking strategies: deterministic soft fusion and stochastic hard fusion, and we compare with previously proposed direct fusion baselines. During testing, the network is able to selectively process the features of the available sensor modalities and produce a trajectory at scale. We present a thorough investigation on the performances on three public autonomous driving, Micro Aerial Vehicle (MAV) and hand-held VIO datasets. The results demonstrate the effectiveness of the fusion strategies, which offer better performances compared to direct fusion, particularly in presence of corrupted data. In addition, we study the interpretability of the fusion networks by visualising the masking layers in different scenarios and with varying data corruption, revealing interesting correlations between the fusion networks and imperfect sensory input data., Comment: Accepted by CVPR 2019
- Published
- 2019
42. Discretized sum-product for large sets
- Author
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Chen, Changhao
- Subjects
Mathematics - Combinatorics ,Mathematics - Classical Analysis and ODEs ,05B99 - Abstract
Let $A\subset [1, 2]$ be a $(\delta, \sigma)$-set with measure $|A|=\delta^{1-\sigma}$ in the sense of Katz and Tao. For $\sigma\in (1/2, 1)$ we show that $$ |A+A|+|AA|\gtrapprox \delta^{-c}|A|, $$ for $c=\frac{(1-\sigma)(2\sigma-1)}{6\sigma+4}$. This improves the bound of Guth, Katz, and Zahl for large $\sigma$., Comment: 12 pages, to appear in Moscow Journal of Combinatorics and Number Theory
- Published
- 2019
- Full Text
- View/download PDF
43. On Large Values of Weyl Sums
- Author
-
Chen, Changhao and Shparlinski, Igor E.
- Subjects
Mathematics - Number Theory ,11K38, 11L15, 28A78, 28A80 - Abstract
A special case of the Menshov--Rademacher theorem implies for almost all polynomials $x_1Z+\ldots +x_d Z^{d} \in {\mathbb R}[Z]$ of degree $d$ for the Weyl sums satisfy the upper bound $$ \left| \sum_{n=1}^{N}\exp\left(2\pi i \left(x_1 n+\ldots +x_d n^{d}\right)\right) \right| \leqslant N^{1/2+o(1)}, \qquad N\to \infty. $$ Here we investigate the exceptional sets of coefficients $(x_1, \ldots, x_d)$ with large values of Weyl sums for infinitely many $N$, and show that in terms of the Baire categories and Hausdorff dimension they are quite massive, in particular of positive Hausdorff dimension in any fixed cube inside of $[0,1]^d$. We also use a different technique to give similar results for sums with just one monomial $xn^d$. We apply these results to show that the set of poorly distributed modulo one polynomials is rather massive as well., Comment: 44 pages, 2 figures
- Published
- 2019
44. Learning with Stochastic Guidance for Navigation
- Author
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Xie, Linhai, Miao, Yishu, Wang, Sen, Blunsom, Phil, Wang, Zhihua, Chen, Changhao, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Robotics - Abstract
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of high variance when applied in complex real world environments. We present a new framework for overcoming these issues by incorporating a stochastic switch, allowing an agent to choose between high and low variance policies. The stochastic switch can be jointly trained with the original DDPG in the same framework. In this paper, we demonstrate the power of the framework in a navigation task, where the robot can dynamically choose to learn through exploration, or to use the output of a heuristic controller as guidance. Instead of starting from completely random moves, the navigation capability of a robot can be quickly bootstrapped by several simple independent controllers. The experimental results show that with the aid of stochastic guidance we are able to effectively and efficiently train DDPG navigation policies and achieve significantly better performance than state-of-the-art baselines models., Comment: A short version is accepted by the NIPS 2018 workshop: Infer2Control
- Published
- 2018
45. Transferring Physical Motion Between Domains for Neural Inertial Tracking
- Author
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Chen, Changhao, Miao, Yishu, Lu, Chris Xiaoxuan, Blunsom, Phil, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics ,Statistics - Machine Learning - Abstract
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data from every domain. To overcome the challenges of domain adaptation on long sensory sequences, we propose a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data. Through the experiments, we demonstrate that it is able to efficiently and effectively convert the raw sequence from a new unlabelled target domain into an accurate inertial trajectory, benefiting from the physical motion knowledge transferred from the labelled source domain. We also conduct real-world experiments to show our framework can reconstruct physically meaningful trajectories from raw IMU measurements obtained with a standard mobile phone in various attachments., Comment: NIPS 2018 workshop on Modeling the Physical World: Perception, Learning, and Control. A complete version will be released soon
- Published
- 2018
46. OxIOD: The Dataset for Deep Inertial Odometry
- Author
-
Chen, Changhao, Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Wei, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and drones. Exploiting inertial data for accurate and reliable navigation and localization has attracted significant research and industrial interest, as IMU measurements are completely ego-centric and generally environment agnostic. Recent studies have shown that the notorious issue of drift can be significantly alleviated by using deep neural networks (DNNs), e.g. IONet. However, the lack of sufficient labelled data for training and testing various architectures limits the proliferation of adopting DNNs in IMU-based tasks. In this paper, we propose and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind data collection for inertial-odometry research, with all sequences having ground-truth labels. Our dataset contains 158 sequences totalling more than 42 km in total distance, much larger than previous inertial datasets. Another notable feature of this dataset lies in its diversity, which can reflect the complex motions of phone-based IMUs in various everyday usage. The measurements were collected with four different attachments (handheld, in the pocket, in the handbag and on the trolley), four motion modes (halting, walking slowly, walking normally, and running), five different users, four types of off-the-shelf consumer phones, and large-scale localization from office buildings. Deep inertial tracking experiments were conducted to show the effectiveness of our dataset in training deep neural network models and evaluate learning-based and model-based algorithms. The OxIOD Dataset is available at: http://deepio.cs.ox.ac.uk
- Published
- 2018
47. A new sum-product estimate in prime fields
- Author
-
Chen, Changhao, Kerr, Bryce, and Mohammadi, Ali
- Subjects
Mathematics - Combinatorics ,Mathematics - Number Theory ,11T99, 11P99 - Abstract
In this paper we obtain a new sum-product estimate in prime fields. In particular, we show that if $A\subseteq \mathbb{F}_p$ satisfies $|A|\le p^{64/117}$ then $$ \max\{|A\pm A|, |AA|\} \gtrsim |A|^{39/32}. $$ Our argument builds on and improves some recent results of Shakan and Shkredov which use the eigenvalue method to reduce to estimating a fourth moment energy and the additive energy $E^+(P)$ of some subset $P\subseteq A+A$. Our main novelty comes from reducing the estimation of $E^+(P)$ to a point-plane incidence bound of Rudnev rather than a point line incidence bound of Stevens and de Zeeuw as done by Shakan and Shkredov., Comment: 16 pages
- Published
- 2018
48. Threshold functions for substructures in random subsets of finite vector spaces
- Author
-
Chen, Changhao and Greenhill, Catherine
- Subjects
Mathematics - Combinatorics - Abstract
The study of substructures in random objects has a long history, beginning with Erd\H{o}s and R\'enyi's work on subgraphs of random graphs. We study the existence of certain substructures in random subsets of vector spaces over finite fields. First we provide a general framework which can be applied to establish coarse threshold results and prove a limiting Poisson distribution at the threshold scale. To illustrate our framework we apply our results to $k$-term arithmetic progressions, sums, right triangles, parallelograms and affine planes. We also find coarse thresholds for the property that a random subset of a finite vector space is sum-free, or is a Sidon set., Comment: 23 pages. This version addresses referees' comments
- Published
- 2018
49. IONet: Learning to Cure the Curse of Drift in Inertial Odometry
- Author
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Chen, Changhao, Lu, Xiaoxuan, Markham, Andrew, and Trigoni, Niki
- Subjects
Computer Science - Robotics ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques., Comment: To appear in AAAI18 (Oral)
- Published
- 2018
50. Finite field analogue of restriction theorem for general measures
- Author
-
Chen, Changhao
- Subjects
Mathematics - Classical Analysis and ODEs ,Mathematics - Combinatorics ,42B05 - Abstract
We study restriction problem in vector spaces over finite fields. We obtain finite field analogue of Mockenhaupt-Mitsis-Bak-Seenger restriction theorem, and we show that the range of the exponentials is sharp., Comment: 8 pages, no figures
- Published
- 2017
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