Title | ||
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Obstacle localization and recognition for autonomous forklifts using omnidirectional stereovision |
Abstract | ||
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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. |
Year | DOI | Venue |
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2015 | 10.1109/IVS.2015.7225739 | 2015 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | Field | DocType |
obstacle localization,obstacle recognition,autonomous forklift,omnidirectional stereovision,industrial environment,3D perception,3D points reconstruction,digital elevation map,DEM,elevation cells,obstacle hypothesis,fast sliding window based approach,monocular fisheye intensity image,tracking mechanism,obstacle classification,boosting,visual codebook type features,classification history,3D visual perception system,AGV | Obstacle,Computer vision,Omnidirectional antenna,Sliding window protocol,Computer science,Boosting (machine learning),Artificial intelligence,Monocular,Grid,Visual perception,Codebook | Conference |
ISSN | Citations | PageRank |
1931-0587 | 1 | 0.36 |
References | Authors | |
14 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
arthur daniel costea | 1 | 3 | 1.41 |
Andrei Vatavu | 2 | 37 | 5.47 |
Sergiu Nedevschi | 3 | 1321 | 126.37 |