Robust Object-based SLAM for High-speed Autonomous Navigation
Author(s)
Ok, Kyel; Liu, Katherine Y; Frey, Kristoffer M. (Kristoffer Martin); How, Jonathan P; Roy, Nicholas
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We present Robust Object-based SLAM for High-speed Autonomous Navigation (ROSHAN), a novel approach to object-level mapping suitable for autonomous navigation. In ROSHAN, we represent objects as ellipsoids and infer their parameters using three sources of information - bounding box detections, image texture, and semantic knowledge - to overcome the observability problem in ellipsoid-based SLAM under common forward-translating vehicle motions. Each bounding box provides four planar constraints on an object surface and we add a fifth planar constraint using the texture on the objects along with a semantic prior on the shape of ellipsoids. We demonstrate ROSHAN in simulation where we outperform the baseline, reducing the median shape error by 83% and the median position error by 72% in a forward-moving camera sequence. We demonstrate similar qualitative result on data collected on a fast-moving autonomous quadrotor.
Date issued
2019-08Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and AstronauticsJournal
2019 International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ok, Kyel et al. "Robust Object-based SLAM for High-speed Autonomous Navigation." 2019 International Conference on Robotics and Automation, May 2019, Montreal, Canada, Institute of Electrical and Electronics Engineers, August 2019 © 2019 IEEE
Version: Author's final manuscript
ISBN
9781538660270
9781538681763
ISSN
2577-087X