Abstract
We present a software tool called a stereovision egomotion sequence generator that was developed for testing visual odometry (VO) algorithms. Various approaches to single and multicamera VO algorithms are reviewed first, and then a reference VO algorithm that has served to demonstrate the program’s features is described. The program offers simple tools for defining virtual static three-dimensional scenes and arbitrary six degrees of freedom motion paths within such scenes and output sequences of stereovision images, disparity ground-truth maps, and segmented scene images. A simple script language is proposed that simplifies tests of VO algorithms for user-defined scenarios. The program’s capabilities are demonstrated by testing a reference VO technique that employs stereoscopy and feature tracking.
Highlights
Motion parameters of an object are given by a set of kinematic quantities defining the object’s movement in relation to its environment
A simple script language simplifies the task of defining 3-D scenes and motion paths that the user can apply for testing various visual odometry (VO) techniques for unconstrained motion trajectories
We hope that stereovision egomotion sequence generator (SESGen) can serve as a useful tool for benchmarking different VO and image segmentation algorithms and can help in better identification of error sources in the tested algorithms
Summary
Motion parameters of an object (further termed egomotion parameters) are given by a set of kinematic quantities defining the object’s movement in relation to its environment. In Ref. 15, a 1-point-random sample consensus (RANSAC) algorithm was employed for estimating vehicle motion from single camera image sequences. In Ref. 6, the RANSAC algorithm was used to estimate the motion of a stereovision unit (720 × 240 pixels image resolution and baseline 28 cm) mounted on an autonomous ground vehicle. For testing the accuracy of the algorithm, a 600-frame simulated image sequence of an urban scene was rendered and the corresponding ground-truth disparity maps were generated. The software enables generation of userdefined 3-D scenes with sequences of egomotion parameters of a freely moving stereovision camera in custom defined scenes and recording of the corresponding stereoscopic and ground-truth disparity maps. This algorithm will serve as a reference method tested by the proposed software tool presented in Sec. 4, in which the main features of the developed program are described.
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