In this paper we propose an approach for obstacle localization and recognition using omnidirectional stereovision applied to autonomous fork-lifts in industrial environments. We use omnidirectional stereovision with two fisheye cameras for the 3D perception of the surrounding environment. Using the reconstructed 3D points, a Digital Elevation Map (DEM) is constructed consisting of a 2.5D grid of elevation cells. Each cell is then classified as ground or obstacle. Further, we use the classified DEM to generate obstacle hypotheses. To ensure a higher detection rate we also propose a fast sliding window based approach relying on the monocular fisheye intensity image. The detections from both approaches are merged and are subjected to a tracking mechanism. Finally each obstacle is classified using boosting over Visual Codebook type features. The classification is refined using the classification history available from tracking. The presented approaches are integrated into a 3D visual perception system for AGVs and are of real time performance.
Obstacle localization and recognition for autonomous forklifts using omnidirectional stereovision
01.06.2015
1480271 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Obstacle detection in urban traffic using stereovision
IEEE | 2005
|Obstacle Detection in Urban Traffic Using Stereovision
British Library Conference Proceedings | 2005
|Obstacle Detection Using Sparse Stereovision and Clustering Techniques
British Library Conference Proceedings | 2012
|