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Inter-agent relative localization is critical for many multi-robot systems operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 3D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional continuously transmitted data (e.g., odometry) leading to potential scaling issues with increased team size and/or decreased communication network capability. By equipping each agent with multiple UWB antennas, our approach addresses these concerns by using only locally collected UWB range measurements, a priori state constraints, and event-based detections of when said constraints are violated. The addition of our learned mean ranging bias correction improves our approach by an additional 19% positional error, and gives us an overall experimental mean absolute position and heading errors of 0.24 m and 9.5° respectively. When compared to other state-of-the-art approaches, our work demonstrates improved performance over similar systems, while remaining competitive with methods that have significantly higher communication costs.

In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision. [ABSTRACT FROM AUTHOR]

In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit the resulting diffraction pattern to have finer and yet simpler image analysis, yielding better estimation of the distance to the object. To test our hypothesis, we build a precision position table and a laser triangulation sensor, generate large data sets to test different estimation algorithms on various materials, and compare data acquisition using a traditional lens versus using a slit. We show that position estimation when using a slit is both more precise and more accurate than comparable methods using a lens. [ABSTRACT FROM AUTHOR]

Both robot and hand‐eye calibration have been object of research for decades. While current approaches manage to precisely and robustly identify the parameters of a robot's kinematic model, they still rely on external devices such as calibration objects, markers and/or external sensors. Instead of trying to fit recorded measurements to a model of a known object, this paper treats robot calibration as an offline SLAM problem, where scanning poses are linked to a fixed point in space via a moving kinematic chain. As such, we enable robot calibration by using nothing but an arbitrary eye‐in‐hand depth sensor. To the authors' best knowledge the presented framework is the first solution to three‐dimensional (3D) sensor‐based robot calibration that does not require external sensors nor reference objects. Our novel approach utilizes a modified version of the Iterative Corresponding Point algorithm to run bundle adjustment on multiple 3D recordings estimating the optimal parameters of the kinematic model. A detailed evaluation of the system is shown on a real robot with various attached 3D sensors. The presented results show that the system reaches precision comparable to a dedicated external tracking system at a fraction of its cost. [ABSTRACT FROM AUTHOR]

Light-weight and accurate mapping is made possible by high-level feature extraction from sensor readings. In this paper, the high-level B-spline features from a 2D LIDAR are extracted with a faster method as a solution to the mapping problem, making it possible for the robot to interact with its environment while navigating. The computation time of feature extraction is very crucial when mobile robots perform real-time tasks. In addition to the existing assessment measures of B-spline feature extraction methods, the paper also includes a new benchmark time metric for evaluating how well the extracted features perform. For point-to-point association, the most reliable vertex control points of the spline features generated from the hints of low-level point feature FALKO were chosen. The standard three indoor and one outdoor data sets were used for the experiment. The experimental results based on benchmark performance metrics, specifically computation time, show that the presented approach achieves better results than the state-of-the-art methods for extracting B-spline features. The classification of the methods implemented in the B-spline features detection and the algorithms are also presented in the paper. [ABSTRACT FROM AUTHOR]

In this paper we present a new way to compute the odometry of a 3D lidar in real-time. Due to the significant relation between these sensors and the rapidly increasing sector of autonomous vehicles, 3D lidars have improved in recent years, with modern models producing data in the form of range images. We take advantage of this ordered format to efficiently estimate the trajectory of the sensor as it moves in 3D space. The proposed method creates and leverages a flatness image in order to exploit the information found in flat surfaces of the scene. This allows for an efficient selection of planar patches from a first range image. Then, from a second image, keypoints related to said patches are extracted. This way, our proposal computes the ego-motion by imposing a coplanarity constraint between pairs whose correspondences are iteratively updated. The proposed algorithm is tested and compared with state-of-the-art ICP algorithms. Experiments show that our proposal, running on a single thread, can run 5× faster than a multi-threaded implementation of GICP, while providing a more accurate localization. A second version of the algorithm is also presented, which reduces the drift even further while needing less than half of the computation time of GICP. Both configurations of the algorithm run at frame rates common for most 3D lidars, 10 and 20 Hz on a standard CPU. [ABSTRACT FROM AUTHOR]

The paper presents a universal architectural pattern and an associated specification method that can be applied in the design of robot control systems. The approach describes the system in terms of embodied agents and proposes a multi-step decomposition enabling precise definition of their inner structure and operation. An embodied agent is decomposed into effectors, receptors, both real and virtual, and a control subsystem. Those entities communicate through communication buffers. The activities of those entities are governed by FSMs that invoke behaviours formulated in terms of transition functions taking as arguments the contents of input buffers and producing the values inserted into output buffers. The method is exemplified by applying it to the design of a control system of a robot executing one of the most important tasks for a service robot, i.e. picking up, by a position–force controlled robot, an object located using an RGB-D image acquired from a Kinect. Moreover in order to substantiate the universality of the presented approach we present how classical, known from the literature, robotic architectures can be expressed as systems composed of one or more embodied agents.

This article presents a 420-GHz phase imaging system designed in a 40-nm CMOS technology. It consists of a transmitter (TX), based on a multiplier chain, and a receiver (RX), based on a two-step IQ down-conversion. Those chips share an external, 17.5 GHz, reference to secure frequency synchronization between them. To increase the overall system signal-to-noise ratio (SNR), the TX is modulated with a low-frequency sine-wave signal, while a two-way LO power combining is implemented for the RX first mixer. Those techniques result in a measured TX effective isotropic radiated power (EIRP) of 10 dBm and RX noise figure (NF) of 27 dB, leading to the overall SNR of 52 dB (at a distance of 25 cm and a resolution bandwidth (RBW) of 100 kHz). Furthermore, the measured phase-detection root-mean-square (rms) $1\sigma $ precision is equal to 1.7° (on a 400 $\circ $ range, at a distance of 25 cm and processing time of 500 ns), which leads to 3.4- $\mu \text{m}$ range resolution. In addition, the system operation is illustrated in two imaging demonstrations, recognition of the printed text on paper and 3-D imaging. Those demonstrations illustrate the potential of the presented system and terahertz (THz) phase imaging in general. [ABSTRACT FROM AUTHOR]

Measuring the distance is one of the main objectives for sensor development. In this article, we present a new sensor system design for depth recovery. It is based on stereopsis with the integrated rotation of an image sensor. Different from conventional stereo vision systems with multiple cameras, the proposed rotational stereo configuration is built upon a single imaging device. By directing its lateral motion with a circular constraint, the visual parallax is generated from multiple viewpoints. The image pairs captured with the integrated sensor rotation are used for the correspondence matching and disparity computation. Moreover, a variation of multiple-baseline stereo with multiple base-angle implementation is proposed. It provides a flexible baseline setting to improve the depth measurement accuracy. The geometric formulation and computational algorithm are derived and developed. In the experiments, a prototype range sensing device is constructed. The results obtained from the real scene environment have demonstrated the feasibility of the proposed depth measurement approach. [ABSTRACT FROM AUTHOR]

Automated three-dimensional (3D) object reconstruction is the task of building a geometric representation of a physical object by means of sensing its surface. Even though new single-view reconstruction techniques can predict the surface, they lead to incomplete models, specially, for non-commons objects such as antique objects or art sculptures. Therefore, to achieve the task's goals, it is essential to automatically determine the locations where the sensor will be placed so that the surface will be completely observed. This problem is known as the next-best-view problem. In this paper, we propose a data-driven approach to address the problem. The proposed approach trains a 3D convolutional neural network (3D CNN) with previous reconstructions in order to regress the position of the next-best-view. To the best of our knowledge, this is one of the first works that directly infers the next-best-view in a continuous space using a data-driven approach for the 3D object reconstruction task. We have validated the proposed approach making use of two groups of experiments. In the first group, several variants of the proposed architecture are analyzed. Predicted next-best-views were observed to be closely positioned to the ground truth. In the second group of experiments, the proposed approach is requested to reconstruct several unseen objects, namely, objects not considered by the 3D CNN during training nor validation. Coverage percentages of up to 90 % were observed. With respect to current state-of-the-art methods, the proposed approach improves the performance of previous next-best-view classification approaches and it is quite fast in running time (3 frames per second), given that it does not compute the expensive ray tracing required by previous information metrics. [ABSTRACT FROM AUTHOR]

This article presents the first surfel-based method for multi-view 3D reconstruction of the boundary between known and unknown space. The proposed approach integrates multiple views from a moving depth camera and it generates a set of surfels that encloses observed empty space, i.e., it models both the boundary between empty and occupied space, and the boundary between empty and unknown space. One novelty of the method is that it does not require a persistent voxel map of the environment to distinguish between unknown and empty space. The problem is solved thanks to an incremental algorithm that computes the Boolean union of two surfel bounded volumes: the known volume from previous frames and the space observed from the current depth image. A number of strategies were developed to cope with errors in surfel position and orientation. The method, implemented on CPU and GPU, was evaluated on real data acquired in indoor scenarios, and it was compared against state of the art approaches. Results show that the proposed method has a low number of false positive and false negatives, it is faster than a standard volumetric algorithm, it has a lower memory consumption, and it scales better in large environments. [ABSTRACT FROM AUTHOR]

Depth sensors have been used extensively for perception in robotics. Typically these sensors have a fixed angular resolution and field of view (FOV). This is in contrast to human perception, which involves foveating : scanning with the eyes' highest angular resolution over regions of interest (ROIs). We build a scanning depth sensor that can control its angular resolution over the FOV. This opens up new directions for robotics research, because many algorithms in localization, mapping, exploration, and manipulation make implicit assumptions about the fixed resolution of a depth sensor, impacting latency, energy efficiency, and accuracy. Our algorithms increase resolution in ROIs either through deconvolutions or intelligent sample distribution across the FOV. The areas of high resolution in the sensor FOV act as artificial fovea and we adaptively vary the fovea locations to maximize a well-known information theoretic measure. We demonstrate novel applications such as adaptive time-of-flight (TOF) sensing, LiDAR zoom, gradient-based LiDAR sensing, and energy-efficient LiDAR scanning. As a proof of concept, we mount the sensor on a ground robot platform, showing how to reduce robot motion to obtain a desired scanning resolution. We also present a ROS wrapper for active simulation for our novel sensor in Gazebo. Finally, we provide extensive empirical analysis of all our algorithms, demonstrating trade-offs between time, resolution and stand-off distance. [ABSTRACT FROM AUTHOR]

The paper presents a universal architectural pattern and an associated specification method that can be applied in the design of robot control systems. The approach describes the system in terms of embodied agents and proposes a multi-step decomposition enabling precise definition of their inner structure and operation. An embodied agent is decomposed into effectors, receptors, both real and virtual, and a control subsystem. Those entities communicate through communication buffers. The activities of those entities are governed by FSMs that invoke behaviours formulated in terms of transition functions taking as arguments the contents of input buffers and producing the values inserted into output buffers. The method is exemplified by applying it to the design of a control system of a robot executing one of the most important tasks for a service robot, i.e. picking up, by a position-force controlled robot, an object located using an RGB-D image acquired from a Kinect. Moreover in order to substantiate the universality of the presented approach we present how classical, known from the literature, robotic architectures can be expressed as systems composed of one or more embodied agents. [ABSTRACT FROM AUTHOR]

Abstract The fringe pattern can realize high-resolution and dense depth sensing. However, the phase ambiguity is a challenge in the fringe pattern. In this paper, one period-code pattern is embedded to the fringe pattern by the frequency-division-multiplexing (FDM) framework. The nonparametric skew and De Bruijn sequence are utilized to determine the label of the period. For a more reliable phase unwrapping, three criteria are utilized to rectify the period numbers further. Quantitative and qualitative experiments show that the proposed method can achieve more reliable depth sensing compared with the counterparts. Even the measured scene contains discontinuous surfaces or sharp edges, the proposed algorithm can attain reliable depth.

Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we present confidence-rich mapping (CRM), a new algorithm for spatial grid-based mapping of the 3D environment. CRM augments the occupancy level at each voxel by its confidence value. By explicitly storing and evolving confidence values using the CRM filter, CRM extends traditional grid mapping in three ways: first, it partially maintains the probabilistic dependence among voxels; second, it relaxes the need for hand-engineering an inverse sensor model and proposes the concept of sensor cause model that can be derived in a principled manner from the forward sensor model; third, and most importantly, it provides consistent confidence values over the occupancy estimation that can be reliably used in collision risk evaluation and motion planning. CRM runs online and enables mapping environments where voxels might be partially occupied. We demonstrate the performance of the method on various datasets and environments in simulation and on physical systems. We show in real-world experiments that, in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence, hence, enabling a more reliable collision risk evaluation for motion planning. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of building descriptive three-dimensional (3-D) maps from sparse and noisy range sensor data. We expand our previously proposed method leveraging Bayesian kernel inference for prediction of occupancy in locations not directly observed by a range sensor. In this paper, we show that our kernel inference approach generalizes previous “counting sensor model” approaches from discrete occupancy grids to continuous maps. Our approach enables prediction about occupancy in regions unobserved by the range sensor based on local measurements, and smoothly transitions to a prior in regions lacking sufficient data for reliable inference. Furthermore, we demonstrate quantitatively using simulated data that the mapping performance of our method can be improved by considering rays as continuous observations, rather than sampling discrete free-space point observations along rays. Though the maps produced by our method are in principle continuous, discretizing space affords us several computational advantages, including the ability to apply recursive Bayesian updates, that allow us to perform inference very efficiently, even on large datasets. To demonstrate this advantage, we present experimental results applying this method to large-scale lidar data collected with a ground robot, showing real-time performance. Other field robotics applications, including underwater 3-D mapping with sonar, are explored qualitatively. [ABSTRACT FROM AUTHOR]

The paper presents a universal architectural pattern and an associated specification method that can be applied in the design of robot control systems. The approach describes the system in terms of embodied agents and proposes a multi-step decomposition enabling precise definition of their inner structure and operation. An embodied agent is decomposed into effectors, receptors, both real and virtual, and a control subsystem. Those entities communicate through communication buffers. The activities of those entities are governed by FSMs that invoke behaviours formulated in terms of transition functions taking as arguments the contents of input buffers and producing the values inserted into output buffers. The method is exemplified by applying it to the design of a control system of a robot executing one of the most important tasks for a service robot, i.e. picking up, by a position-force controlled robot, an object located using an RGB-D image acquired from a Kinect. Moreover in order to substantiate the universality of the presented approach we present how classical, known from the literature, robotic architectures can be expressed as systems composed of one or more embodied agents. [ABSTRACT FROM AUTHOR]

In this paper, we present two novel approaches, super rays and culling region, for efficiently updating grid-based occupancy maps with point clouds. Rays, which traverse from the sensor origin to the sensor data, update the occupancy probabilities of a map representing an environment. Based on the ray model, we define a super ray as a representative ray to multiple rays having the same traversal patterns during the map updates. Our super rays utilize the geometric information of rays and reduce the number of points used for updating the map. For constructing super rays efficiently, we propose mapping lines for handling two- and three-dimensional cases from an observation that edges or grid points branch out the traversal patterns on the map. Furthermore, we introduce a culling region using the occupancy states of the updated map for reducing redundant computations occurred in updates. The super rays perform the update process in a single traversal, and the culling region reduces the number of unnecessary traversals for updating the map. As a result, our combined method improves the update performance without compromising any representation accuracy of a grid-based map. We test the update performance of the proposed method using public indoor and outdoor datasets. Our combined approach shows up to 11.8 times and 2.8 times performance improvement over the state-of-the-art update methods of grid-based maps in the indoor and outdoor scenes, respectively. Also, we compare the update speed and the representation accuracy of our method using the KITTI dataset over the state-of-the-art learning-based occupancy maps. In a navigation scenario that raw point clouds are acquired in 10 Hz, our method shows the best performance on the update speed and thus the highest representation accuracy within a given time. [ABSTRACT FROM AUTHOR]

Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors.

This paper presents a dense method for estimating planar motion with a laser scanner. Starting from a symmetric representation of geometric consistency between scans, we derive a precise range flow constraint and express the motion of the scan observations as a function of the rigid motion of the scanner. In contrast to existing techniques, which align the incoming scan with either the previous one or the last selected keyscan, we propose a combined and efficient formulation to jointly align all these three scans at every iteration. This new formulation preserves the advantages of keyscan-based strategies but, is more robust against suboptimal selection of keyscans and the presence of moving objects. An extensive evaluation of our method is presented with simulated and real data in both static and dynamic environments. Results show that our approach is one order of magnitude faster and significantly more accurate than existing methods in all the conducted experiments. With a runtime of about one millisecond, it is suitable for those robotic applications that require planar odometry with low computational cost. The code is available online as a ROS package. [ABSTRACT FROM AUTHOR]

As one of the most popular range detection methods, lidar is commonly used in various robotic applications. Although most robotic platforms easily adopt 2D lidar for range sensing, 3D lidar is rarely used in mobile robots, owing to its high cost. Some methods reported in the literature obtain 3D range information by rotating a single 2D lidar device. However, for most of these methods, there is a trade-off between 3D scan frequency and measurement density. Existing methods discussed in the literature for increasing the measurement density in high-frequency lidar have high time complexity and require certain conditions on data distribution. In a previous work, we showed the usability of an image super-resolution method, the Papoulis–Gerchberg (P–G) algorithm, on range data represented in the form of a greyscale image. However, the low convergence rate of the original P–G algorithm impedes its use for online applications. In this study, we advanced the P–G algorithm to drastically reduce the convergence time and improve performance by utilizing previous range images. The proposed algorithm now supports application on a mobile robot with online measurement density enhancement for 3D range images collected by rotating a 2D lidar device around its pitch axis with a high 3D scan frequency. We show illustrative examples for different scenarios to present the effectiveness of the proposed method on a 3D range sensor mounted on a mobile robot. [ABSTRACT FROM AUTHOR]

Image sensors have widespread use in many robotics applications and, in particular, in target tracking. While existing methods assume continuous image feedback, the novelty of this brief stems from the development of dwell time conditions to guarantee convergence of state estimates to an ultimate bound for a class of image-based observers in the presence of intermittent measurements. A Lyapunov analysis for the switched system is performed to develop convergence conditions based on the minimum amount of time the object must be visible and on the maximum amount of time the object may remain outside the camera view. Experimental results are included to verify the theoretical results and elucidate real-world performance. [ABSTRACT FROM AUTHOR]

One of the major challenges for mobile robots in human-shaped environments is navigating stairways. This study presents a method for accurately detecting, localizing, and estimating the characteristics of stairways using point cloud data. The main challenge is the wide variety of different structures and shapes of stairways. This challenge is often aggravated by an unfavorable position of the sensor, which leaves large parts of the stairway occluded. This can be further aggravated by sparse point data. We overcome these difficulties by introducing a three-dimensional graph-based stairway-detection method combined with competing initializations. The stairway graph characterizes the general structural design of stairways in a generic way that can be used to describe a large variety of different stairways. By using multiple ways to initialize the graph, we can robustly detect stairways even if parts of the stairway are occluded. Furthermore, by letting the initializations compete against each other, we find the best initialization that accurately describes the measured stairway. The detection algorithm utilizes a plane-based approach. We also investigate different planar segmentation algorithms and experimentally compare them in an application-orientated manner. Our system accurately detects and estimates the stairway parameters with an average error of only 2 . 5 mm for a variety of stairways including ascending, descending, and spiral stairways. Our method works robustly with different depth sensors for either small- or large-scale environments and for dense and sparse point cloud data. Despite this generality, our system's accuracy is higher than most state-of-the-art stairway-detection methods. [ABSTRACT FROM AUTHOR]

Lifting objects, whose mass may produce high wrist torques that exceed the hardware strength limits, could lead to unstable grasps or serious robot damage. This work introduces a new Center-of-Mass (CoM)-based grasp pose adaptation method, for picking up objects using a combination of exteroceptive 3D perception and proprioceptive force/torque sensor feedback. The method works in two iterative stages to provide reliable and wrist torque efficient grasps. Initially, a geometric object CoM is estimated from the input range data. In the first stage, a set of hand-size handle grasps are localized on the object and the closest to its CoM is selected for grasping. In the second stage, the object is lifted using a single arm, while the force and torque readings from the sensor on the wrist are monitored. Based on these readings, a displacement to the new CoM estimation is calculated. The object is released and the process is repeated until the wrist torque effort is minimized. The advantage of our method is the blending of both exteroceptive (3D range) and proprioceptive (force/torque) sensing for finding the grasp location that minimizes the wrist effort, potentially improving the reliability of the grasping and the subsequent manipulation task. We experimentally validate the proposed method by executing a number of tests on a set of objects that include handles, using the humanoid robot WALK-MAN. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e. a sequence of views that minimizes the trace of the targets’ cumulative state covariance, constructed using a realistic model of the stereo rig that captures image quantization noise and a Kalman Filter (KF) that fuses the observation history with new information.The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF. [ABSTRACT FROM AUTHOR]

Underwater sensing using electric field has been traditionally modeled by the computationally heavy finite element method (FEM) or boundary element method. In this paper, techniques in light scattering problems were for the first time expanded to underwater active electrosense. Numerical solutions of the volume integral equation were formulated, including a method of moment and discrete dipole approximation (DDA). We found that only DDA was suitable for conductive mediums and the connection between DDA, and a well-known sphere perturbation formula was further established. DDA was found to perform more than 100 times faster than FEM with only a 10% relative difference in underwater electrosense problems. A DDA-based unscented Kalman filter was proposed for inferring object size and location, and a one-dimensional sensor was made for further experimental validation. This paper lays a solid foundation for real-world underwater electrosensors. [ABSTRACT FROM AUTHOR]

Extrinsic calibration of a camera and a 2D laser range finder (lidar) sensors is crucial in sensor data fusion applications; for example SLAM algorithms used in mobile robot platforms. The fundamental challenge of extrinsic calibration is when the camera-lidar sensors do not overlap or share the same field of view. In this paper we propose a novel and flexible approach for the extrinsic calibration of a camera-lidar system without overlap, which can be used for robotic platform self-calibration. The approach is based on the robot-world hand-eye calibration (RWHE) problem; proven to have efficient and accurate solutions. First, the system was mapped to the RWHE calibration problem modeled as the linear relationship AX = ZB, where X and Z are unknown calibration matrices. Then, we computed the transformation matrix B, which was the main challenge in the above mapping. The computation is based on reasonable assumptions about geometric structure in the calibration environment. The reliability and accuracy of the proposed approach is compared to a state-of-the-art method in extrinsic 2D lidar to camera calibration. Experimental results from real datasets indicate that the proposed approach provides better results with an L2 norm translational and rotational deviations of 314 mm and 0.12° respectively. [ABSTRACT FROM AUTHOR]

Switched systems theory is used to analyze the stability of image-based observers for three-dimensional localization of objects in a scene in the presence of intermittent measurements due to occlusions, feature tracking losses, or a limited camera field of view, for example. Generally, observers or filters that are exponentially stable under persistent measurement availability may have unbounded error growth under intermittent measurement loss, even while providing seemingly accurate state estimates. By constructing a framework that utilizes a state predictor during periods when measurements are not available, a class of image-based observers is shown to be exponentially convergent in the presence of intermittent measurements if an average dwell time, and a total unmeasurability time, condition is satisfied. The conditions are developed in a general form, applicable to any observer that is exponentially convergent assuming persistent visibility, and utilizes object motion knowledge to reduce the amount of time measurements must be available to maintain convergence guarantees. Based on the stability results, simulations are provided to show improved performance compared to a zero-order hold approach, where state estimates are held constant when measurements are not available. Experimental results are also included to verify the theoretical results, to demonstrate applicability of the developed observer and predictor design, and to compare against a typical approach using an extended Kalman filter. [ABSTRACT FROM PUBLISHER]

An objective error criterion is proposed for evaluating the accuracy of maps of unknown environments acquired by making range measurements with different sensing modalities and processing them with different techniques. The criterion can also be used for the assessment of goodness of fit of curves or shapes fitted to map points. A demonstrative example from ultrasonic mapping is given based on experimentally acquired time-of-flight measurements and compared with a very accurate laser map, considered as absolute reference. The results of the proposed criterion are compared with the Hausdorff metric and the median error criterion results. The error criterion is sufficiently general and flexible that it can be applied to discrete point maps acquired with other mapping techniques and sensing modalities as well.

Visual odometry can be augmented by depth information such as provided by RGB-D cameras, or from lidars associated with cameras. However, such depth information can be limited by the sensors, leaving large areas in the visual images where depth is unavailable. Here, we propose a method to utilize the depth, even if sparsely available, in recovery of camera motion. In addition, the method utilizes depth by structure from motion using the previously estimated motion, and salient visual features for which depth is unavailable. Therefore, the method is able to extend RGB-D visual odometry to large scale, open environments where depth often cannot be sufficiently acquired. The core of our method is a bundle adjustment step that refines the motion estimates in parallel by processing a sequence of images, in a batch optimization. We have evaluated our method in three sensor setups, one using an RGB-D camera, and two using combinations of a camera and a 3D lidar. Our method is rated #4 on the KITTI odometry benchmark irrespective of sensing modality-compared to stereo visual odometry methods which retrieve depth by triangulation. The resulting average position error is 1.14 % of the distance traveled. [ABSTRACT FROM AUTHOR]

The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps in two fundamental aspects: (1) it does not assume an a priori discrimination of the world into grid cells and therefore can provide maps at an arbitrary resolution; (2) it captures spatial relationships between measurements naturally, thus being more robust to outliers and possessing better generalization performance. The technique, named Hilbert maps, is based on the computation of fast kernel approximations that project the data in a Hilbert space where a logistic regression classifier is learnt. We show that this approach allows for efficient stochastic gradient optimization where each measurement is only processed once during learning in an online manner. We present results with three types of approximations: random Fourier; Nyström; and a novel sparse projection. We also extend the approach to accept probability distributions as inputs, for example, due to uncertainty over the position of laser scans due to sensor or localization errors. In this extended version, experiments were conducted in two dimensions and three dimensions, using popular benchmark datasets. Furthermore, an analysis of the adaptive capabilities of the technique to handle large changes in the data, such as trajectory update before and after loop closure during simultaneous localization and mapping, is also included. [ABSTRACT FROM AUTHOR]

This paper proposes a calibration method that accurately estimates six parameters between the two centers of 2-D light detection and ranging (LIDAR) and a rotating platform. This method uses a simple plane, and to the best of our knowledge, it is the first to enable full-degree-of-freedom (DOF) estimation without additional hardware. The key concept behind this method is a decoupling property, in which the direction of a line on a plane does not contain 3-DOF translation terms. Based on this, a cost function for rotation is constructed, and 3-DOF rotation parameters are estimated. With this rotation, the remaining 3-DOF translation parameters are calculated in a manner that minimizes the cost function for translation only. In other words, an original 6-DOF problem is decoupled into two 3-DOF estimation problems. Given these cost functions, degenerate cases are mathematically analyzed for known cases (incomplete), and the robustness is numerically tested for all possible cases (complete). The performance of the method is validated by extensive simulations and experimentations, and the estimated parameters from the proposed method demonstrate better accuracy than previous methods. [ABSTRACT FROM AUTHOR]

We present an object pose estimation approach exploiting both geometric depth and photometric color information available from an RGB-D sensor. In contrast to various efforts relying on object segmentation with a known background structure, our approach does not depend on the segmentation and thus exhibits superior performance in unstructured environments. Inspired by a voting-based approach employing an oriented point pair feature, we present a voting-based approach which further incorporates color information from the RGB-D sensor and which exploits parallel power of the modern parallel computing architecture. The proposed approach is extensively evaluated with three state-of-the-art approaches on both synthetic and real datasets, and our approach outperforms the other approaches in terms of both computation time and accuracy. [ABSTRACT FROM AUTHOR]

This work discusses an innovative approach to imaging which can improve the robustness of existing active-range measurement methods and potentially enhance their use in a variety of outdoor applications. By merging a proven modulation technique from the domain of spread-spectrum communications with the bleeding-edge CMOS sensor technology, the prototype of the modulated range sensor is designed and evaluated. A suitable set of application-specific spreading codes is proposed, evaluated and tested on the prototype. Experimental results show that the introduced modulation technique significantly reduces the impacts of environmental factors such as sunlight and external light sources, as well as mutual interference of identical devices. The proposed approach can be considered as a promising basis for a new generation of robust and cost-efficient range-sensing solutions for automotive applications, autonomous vehicles or robots. [ABSTRACT FROM AUTHOR]

We present a new approach to detection and tracking of moving objects with a 2D laser scanner for autonomous driving applications. Objects are modelled with a set of rigidly attached sample points along their boundaries whose positions are initialized with and updated by raw laser measurements, thus allowing a non-parametric representation that is capable of representing objects independent of their classes and shapes. Detection and tracking of such object models are handled in a theoretically principled manner as a Bayes filter where the motion states and shape information of all objects are represented as a part of a joint state which includes in addition the pose of the sensor and geometry of the static part of the world. We derive the prediction and observation models for the evolution of the joint state, and describe how the knowledge of the static local background helps in identifying dynamic objects from static ones in a principled and straightforward way. Dealing with raw laser points poses a significant challenge to data association. We propose a hierarchical approach, and present a new variant of the well-known Joint Compatibility Branch and Bound algorithm to respect and take advantage of the constraints of the problem introduced through correlations between observations. Finally, we calibrate the system systematically on real world data containing 7,500 labelled object examples and validate on 6,000 test cases. We demonstrate its performance over an existing industry standard targeted at the same problem domain as well as a classical approach to model-free object tracking. [ABSTRACT FROM AUTHOR]