LiDARと深度カメラに対応した視覚慣性SLAMによるサブマップベースのMAV自律探索
Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras
サブマップ単位の視覚慣性SLAMとサンプリングベースの次善視点計画を組み合わせ、LiDARまたは深度カメラのどちらでも動くMAVの自律探索フレームワークを提案し、シミュレーションと実機で有効性を示した。
著者: Sotiris Papatheodorou, Simon Boche, Sebastián Barbas Laina, Stefan Leutenegger
分類: cs.RO
原文アブストラクト
Autonomous exploration of unknown space is an essential component for the deployment of mobile robots in the real world. Safe navigation is crucial for all robotics applications and requires accurate and consistent maps of the robot's surroundings. To achieve full autonomy and allow deployment in a wide variety of environments, the robot must rely on on-board state estimation which is prone to drift over time. We propose a Micro Aerial Vehicle (MAV) exploration framework based on local submaps to allow retaining global consistency by applying loop-closure corrections to the relative submap poses. To enable large-scale exploration we efficiently compute global, environment-wide frontiers from the local submap frontiers and use a sampling-based next-best-view exploration planner. Our method seamlessly supports using either a LiDAR sensor or a depth camera, making it suitable for different kinds of MAV platforms. We perform comparative evaluations in simulation against a state-of-the-art submap-based exploration framework to showcase the efficiency and reconstruction quality of our approach. Finally, we demonstrate the applicability of our method to real-world MAVs, one equipped with a LiDAR and the other with a depth camera. Video available at https://youtu.be/Uf5fwmYcuq4 .
