Active velocity estimation using light curtains via self-supervised multi-armed bandits
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10514_2024_10168_ReferencePDF.pdf
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Author(s)
Ancha, Siddharth
Pathak, Gaurav
Zhang, Ji
Narasimhan, Srinivasa
Held, David
Date Issued
August 10, 2024
Journal
Autonomous Robots
Publisher
Springer US
Citation
Ancha, S., Pathak, G., Zhang, J. et al. Active velocity estimation using light curtains via self-supervised multi-armed bandits. Auton Robot 48, 15 (2024).
Version
Author's final manuscript
Abstract
To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments.
MIT Department
MIT Quest for Intelligence
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10514-024-10168-2
