MAVの反応的衝突回避のための低遅延マップレスESDFクエリ
Low-Latency Mapless ESDF Queries for MAV Reactive Collision Avoidance
ポーズ付き深度画像から直接GPUでESDFを計算し、地図構築なしで低遅延・高精度な衝突回避を実現する手法を提案。森林環境での飛行実験で有効性を示した。
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著者: Leonard Freißmuth, Cedric Le Gentil, Helen Oleynikova, Stefan Leutenegger
分類: cs.RO
原文アブストラクト
Reactive collision avoidance in unknown, cluttered environments must perceive obstacles and correct motion within milliseconds. Existing methods rely on maps for high-rate collision queries, but their acceleration structures require pre- processing. That cost is amortized in long-horizon planning, where a map serves millions of queries; reactive planning, however, is a near-field, short-horizon problem with potentially only hundreds of queries per measurement, where building a map may not pay off. We propose a mapless, Graphics Processing Unit (GPU)-accelerated Euclidean Signed Distance Field (ESDF) query with negligible pre-processing that operates directly on posed depth images, with resolution set by pooling the image rather than by a voxel size. On an embedded GPU it is simultaneously more accurate and lower in total latency than every voxel-grid configuration we measured between 2 cm and 20 cm, at one to two orders of magnitude less memory at matched resolution. With our query integrated into a Model Predictive Controller (MPC), simulated runs in laser-scanned forests at up to 9 m/s collide in 0.4 % through dense branches, against 5.9 % with the best voxel grid and 5.5 % with the best mapless baseline at our resolution, whose failures we attribute to voxel resolution and total latency respectively. We deploy the system onboard at two forest sites and fly at commanded speeds of up to 4 m/s.