Evaluation of a Stereo Visual Odometry Algorithm for Passenger Vehicle Navigation University of Michigan - Dearborn

Format:

Conference/Event

Author/Creator:

Aladem, Aladem, author.

Contributor:

Rawashdeh, Nathir

Rawashdeh, Samir

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractTo reliably implement driver-assist features and ultimately self-driving cars, autonomous driving systems will likely rely on a variety of sensor types including GPS, RADAR, LASER range finders, and cameras. Cameras are an essential sensory component because they lend themselves to the task of identifying object types that a self-driving vehicle is likely to encounter such as pedestrians, cyclists, animals, other cars, or objects on the road. In this paper, we present a feature-based visual odometry algorithm based on a stereo-camera to perform localization relative to the surrounding environment for purposes of navigation and hazard avoidance. Using a stereo-camera enhances the accuracy with respect to monocular visual odometry. The algorithm relies on tracking a local map consisting of sparse 3D map points. By tracking this map across frames, the algorithm makes use of the full history of detected features which reduces the drift in the estimated motion trajectory. This is unlike traditional visual odometry algorithms that track features and thus estimate motion only from frame-to-frame. The visual odometry algorithm is evaluated using the widely used KITTI dataset and benchmarking suite (project by Karlsruhe Institute of Technology and Toyota Technological Institute). Relevant performance metrics are discussed and computed motion trajectories are shown

Notes:

Vendor supplied data

Publisher Number:

2017-01-0046

Access Restriction:

Restricted for use by site license