Active Perception using Light Curtains for Autonomous Driving

TL;DR

This paper introduces an active perception method for autonomous driving that uses light curtains guided by deep learning uncertainty to efficiently detect objects in 3D environments.

Contribution

It presents a novel approach combining light curtains with uncertainty-guided optimization for improved 3D object recognition.

Findings

Enhanced detection accuracy through active, uncertainty-guided sensor placement

Efficient optimization algorithm for sensor placement respecting device constraints

Demonstrated effectiveness in sequential object detection tasks

Abstract

Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using light curtains, a resource-efficient controllable sensor that measures depth at user-specified locations in the environment. Crucially, we propose using prediction uncertainty of a deep learning based 3D point cloud detector to guide active perception. Given a neural network's uncertainty, we derive an optimization objective to place light curtains using the principle of maximizing information gain. Then, we develop a novel and efficient optimization algorithm to maximize this objective by encoding the physical constraints of the device into a constraint graph and optimizing with dynamic programming. We show how a 3D detector can be trained to detect objects in a scene by sequentially placing uncertainty-guided light curtains to successively improve detection accuracy. Code and details can be found on the project webpage: http://siddancha.github.io/projects/active-perception-light-curtains.