Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723722
Title: Automatic dense 3D scene mapping from non-overlapping passive visual sensors for future autonomous systems
Author: Hamilton, Oliver
ISNI:       0000 0004 6420 9970
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2017
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Abstract:
The ever increasing demand for higher levels of autonomy for robots and vehicles means there is an ever greater need for such systems to be aware of their surroundings. Whilst solutions already exist for creating 3D scene maps, many are based on active scanning devices such as laser scanners and depth cameras that are either expensive, unwieldy, or do not function well under certain environmental conditions. As a result passive cameras are a favoured sensor due their low cost, small size, and ability to work in a range of lighting conditions. In this work we address some of the remaining research challenges within the problem of 3D mapping around a moving platform. We utilise prior work in dense stereo imaging, Stereo Visual Odometry (SVO) and extend Structure from Motion (SfM) to create a pipeline optimised for on vehicle sensing. Using forward facing stereo cameras, we use state of the art SVO and dense stereo techniques to map the scene in front of the vehicle. With significant amounts of prior research in dense stereo, we addressed the issue of selecting an appropriate method by creating a novel evaluation technique. Visual 3D mapping of dynamic scenes from a moving platform result in duplicated scene objects. We extend the prior work on mapping by introducing a generalized dynamic object removal process. Unlike other approaches that rely on computationally expensive segmentation or detection, our method utilises existing data from the mapping stage and the findings from our dense stereo evaluation. We introduce a new SfM approach that exploits our platform motion to create a novel dense mapping process that exceeds the 3D data generation rate of state of the art alternatives. Finally, we combine dense stereo, SVO, and our SfM approach to automatically align point clouds from non-overlapping views to create a rotational and scale consistent global 3D model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.723722  DOI: Not available
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