連続SE(3)軌道上でのシャノン相互情報量の勾配上昇最適化による能動マッピング
Active Mapping via Gradient Ascent Optimization of Shannon Mutual Information over Continuous SE(3) Trajectories
グリッドマップとレイ観測の間のシャノン相互情報量を微分可能に近似し、SE(3)連続空間で勾配上昇により情報量の多いセンシング軌道を計画する手法を提案した。
著者: Arash Asgharivaskasi, Shumon Koga, Nikolay Atanasov
分類: cs.RO
原文アブストラクト
The problem of active mapping aims to plan an informative sequence of sensing views given a limited budget such as distance traveled. This paper consider active occupancy grid mapping using a range sensor, such as LiDAR or depth camera. State-of-the-art methods optimize information-theoretic measures relating the occupancy grid probabilities with the range sensor measurements. The non-smooth nature of ray-tracing within a grid representation makes the objective function non-differentiable, forcing existing methods to search over a discrete space of candidate trajectories. This work proposes a differentiable approximation of the Shannon mutual information between a grid map and ray-based observations that enables gradient ascent optimization in the continuous space of SE(3) sensor poses. Our gradient-based formulation leads to more informative sensing trajectories, while avoiding occlusions and collisions. The proposed method is demonstrated in simulated and real-world experiments in 2-D and 3-D environments.
