DeepAI AI Chat
Log In Sign Up

Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits

by   Siddharth Ancha, et al.

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. Project website:


page 4

page 5

page 7

page 8

page 14

page 15

page 19

page 21


Active Safety Envelopes using Light Curtains with Probabilistic Guarantees

To safely navigate unknown environments, robots must accurately perceive...

Active Perception using Light Curtains for Autonomous Driving

Most real-world 3D sensors such as LiDARs perform fixed scans of the ent...

IVO: Inverse Velocity Obstacles for Real Time Navigation

In this paper, we present "IVO: Inverse Velocity Obstacles" an ego-centr...

Top-K Ranking Deep Contextual Bandits for Information Selection Systems

In today's technology environment, information is abundant, dynamic, and...

Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks

This paper presents a method to learn the Cartesian velocity of objects ...