AdVENTR: 複雑な屋外環境における自律ロボットナビゲーション
AdVENTR: Autonomous Robot Navigation in Complex Outdoor Environments
不整地や植生のある屋外環境で、車輪型・脚型ロボットが走行できるよう、RGBカメラや3D Lidarなどのセンサデータを用いた学習ベースの知覚・計画アルゴリズムと、シーンに応じて知覚手法を切り替えるシステムを開発し、HuskyとSpotで実証した。
著者: Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel system, AdVENTR for autonomous robot navigation in unstructured outdoor environments that consist of uneven and vegetated terrains. Our approach is general and can enable both wheeled and legged robots to handle outdoor terrain complexity including unevenness, surface properties like poor traction, granularity, obstacle stiffness, etc. We use data from sensors including RGB cameras, 3D Lidar, IMU, robot odometry, and pose information with efficient learning-based perception and planning algorithms that can execute on edge computing hardware. Our system uses a scene-aware switching method to perceive the environment for navigation at any time instant and dynamically switches between multiple perception algorithms. We test our system in a variety of sloped, rocky, muddy, and densely vegetated terrains and demonstrate its performance on Husky and Spot robots.
