NDLI: Robust visual odometry estimation of road vehicle from dominant surfaces for large-scale mapping

Content Provider	IET Digital Library
Author	Siddiqui, Rafid Khatibi, Siamak
Abstract	Every urban environment contains a rich set of dominant surfaces which can provide a solid foundation for visual odometry estimation. In this work visual odometry is robustly estimated by computing the motion of camera mounted on a vehicle. The proposed method first identifies a planar region and dynamically estimates the plane parameters. The candidate region and estimated plane parameters are then tracked in the subsequent images and an incremental update of the visual odometry is obtained. The proposed method is evaluated on a navigation dataset of stereo images taken by a car mounted camera that is driven in a large urban environment. The consistency and resilience of the method has also been evaluated on an indoor robot dataset. The results suggest that the proposed visual odometry estimation can robustly recover the motion by tracking a dominant planar surface in the Manhattan environment. In addition to motion estimation solution a set of strategies are discussed for mitigating the problematic factors arising from the unpredictable nature of the environment. The analyses of the results as well as dynamic environmental strategies indicate a strong potential of the method for being part of an autonomous or semi-autonomous system.
Starting Page	314
Ending Page	322
Page Count	9
ISSN	1751956X
Volume Number	9
e-ISSN	17519578
Issue Number	Issue 3, Apr (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/9/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2014.0100
Journal	IET Intelligent Transport Systems
Publisher Date	2014-09-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Camera Candidate Planar Region Car Mounted Camera Computer Vision And Image Processing Technique Distance Measurement Dominant Planar Surface Tracking Dynamic Environmental Strategy Dynamic Estimation Image Recognition Image Sonsor Indoor Robot Dataset Large-scale Mapping Manhattan Environment Motion Estimation Motion Recovery Moving Vehicle Motion Navigation Dataset Object Tracking Plane Pose Parameter Pose Estimation Problematic Factors Road Safety Road Traffic System Control Road Vehicle Robust Visual Odometry Estimation Method Semiautonomous System Spatial Variables Measurement Stereo Image Stereo Image Processing Stereo Vision Camera Transportation Urban Environment
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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