Robust Object-based SLAM for High-speed Autonomous Navigation
Name
OkLiu19icra.pdf
Description
Accepted version
Size
5.15 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Ok, Kyel
Liu, Katherine Y
Frey, Kristoffer M. (Kristoffer Martin)
How, Jonathan P
Roy, Nicholas
Date Issued
August 2019
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2019.8794344
