Robust Object-based SLAM for High-speed Autonomous Navigation
Kyel Ok, Katherine Liu, Kris Frey, Jonathan P. How, Nicholas Roy
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.
BibTeX
@inproceedings{icra2019_robustobjectbase,
title = {Robust Object-based SLAM for High-speed Autonomous Navigation},
author = {Kyel Ok and Katherine Liu and Kris Frey and Jonathan P. How and Nicholas Roy},
booktitle = {ICRA 2019},
year = {2019}
}