ライトカーテンを用いた能動的知覚による自動運転
Active Perception using Light Curtains for Autonomous Driving
深層学習3D検出器の不確実性を手がかりに、ライトカーテンと呼ばれる制御可能な深度センサの配置を最適化し、逐次的に物体検出精度を高める能動的知覚手法を提案した。
著者: Siddharth Ancha, Yaadhav Raaj, Peiyun Hu, Srinivasa G. Narasimhan, David Held
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
