Wednesday, March 25
11:00 am to 12:30 pm
3305 Newell-Simon Hall
11:00 am to 12:30 pm
3305 Newell-Simon Hall
Robust, Reliable Robot Odometry and its Certification
Abstract:
Robot odometry is the backbone of nearly all modern autonomous systems including, but not limited to, unmanned aerial vehicles, autonomous underwater vehicles, and autonomous ground vehicles. Most downstream tasks such as path planning, perception, and control require accurate knowledge of the vehicle position and orientation at any given moment. While odometry is well-studied and has many potential solutions, due to its high-importance to the rest of the autonomy stack, any increase in robustness and accuracy will only further drive the reliability and stability of the given autonomous system.
There are a number of areas where current odometry methods can be improved in accuracy, robustness, or reliability. More specifically, we find that poor sensor models or assumptions can often reduce reliability and accuracy. Another area of concern is sensor failure, where odometry methods that are too tightly coupled often fail entirely with a single sensor outage. Finally, another potential issue is when odometry failures do occur, most downstream tasks are unaware, which can lead to erroneous behavior.
In this work we present methods that overcome these challenges. Specifically, they (1) seek to correct any modeling errors that may occur, (2) are robust to sensor failure, and (3) provide sub-optimality metrics for downstream tasks. We first present a method for fusion of wheel encoder measurements for off-road autonomous vehicles that provides piecewise-planar constraints for non-planar environments, while estimating wheel slip, wheel radii, and wheel baseline all in real-time. Additionally, we present a novel LiDAR odometry method, with a frontend informed by our empirical evaluations, and a backend that smooths over a window of prior states while providing map corrections in real-time. This results in more accurate estimates and heightened robustness.
Finally, we propose finding a fast “sufficient condition” certificate for these optimization-based odometry methods utilizing novel semidefinite programming techniques. While not a perfect catch-all for odometry failures, it aims to detect when sub-optimality or degeneracies in state estimation may be occurring and pass this information to downstream tasks, allowing for reactionary behavior.
Thesis Committee Members:
Michael Kaess, chair
Sebastian Scherer
George Kantor
Tim Barfoot, University of Toronto
George Kantor
Tim Barfoot, University of Toronto