Search

Your search keyword '"Upcroft, Ben"' showing total 221 results
Start Over Author "Upcroft, Ben"

Refine your results

221 results on '"Upcroft, Ben"'

216. Nonparametric semantic segmentation for 3D street scenes.

Author

He, Hu and Upcroft, Ben

Abstract

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth. [ABSTRACT FROM PUBLISHER]

Published
2013
Full Text
View/download PDF

218. Learning Informative Features for Indoor Traversability.

Author

Khatib, Oussama, Kumar, Vijay, Rus, Daniela, Brooks, Alex, Makarenko, Alexei, Upcroft, Ben, and Durrant-Whyte, Hugh

Abstract

This paper presents a method for automatic terrain classification, using a cheap monocular camera in conjunction with a robot's stall sensor. A first step is to have the robot generate a training set of labelled images. Several techniques are then evaluated for preprocessing the images, reducing their dimensionality, and building a classifier. Finally, the classifier is implemented and used online by an indoor robot. Results are presented, demonstrating an increased level of autonomy. [ABSTRACT FROM AUTHOR]

Published
2008
Full Text
View/download PDF

219. Automatic object segmentation of unstructured scenes using colour and depth maps.

Author

Hu He and Upcroft, Ben

Subjects
*IMAGE segmentation, *DEPTH maps (Digital image processing), *ROBOTICS, *VIDEO processing, *INFORMATION resources
Abstract

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
View/download PDF

221. A synthesizable hardware evolutionary algorithm design for unmanned aerial system real-time path planning

Author

Kok, J., Gonzalez, L. F., Walker, R., Gurnett, T., Neil Kelson, Wyeth, Gordon, and Upcroft, Ben

Subjects
090199 Aerospace Engineering not elsewhere classified
Abstract

The main objective of this paper is to detail the development of a feasible hardware design based on Evolutionary Algorithms (EAs) to determine flight path planning for Unmanned Aerial Vehicles (UAVs) navigating terrain with obstacle boundaries. The design architecture includes the hardware implementation of Light Detection And Ranging (LiDAR) terrain and EA population memories within the hardware, as well as the EA search and evaluation algorithms used in the optimizing stage of path planning. A synthesisable Very-high-speed integrated circuit Hardware Description Language (VHDL) implementation of the design was developed, for realisation on a Field Programmable Gate Array (FPGA) platform. Simulation results show significant speedup compared with an equivalent software implementation written in C++, suggesting that the present approach is well suited for UAV real-time path planning applications.

Published
2010

Catalog

Books, media, physical & digital resources
See catalog results