论文标题
通过连续SE（3）轨迹对香农共同信息的梯度上升优化的主动映射
Active Mapping via Gradient Ascent Optimization of Shannon Mutual Information over Continuous SE(3) Trajectories
论文作者
论文摘要
主动映射的问题旨在计划有限的预算（例如旅行距离）的信息传感视图的信息顺序。本文考虑使用范围传感器（例如LiDar或Depth Camera）的主动占用网格映射。最先进的方法优化了将占用网格概率与范围传感器测量相关的信息理论措施。网格表示中射线追踪的非平滑性质使目标函数不可差异，从而迫使现有方法在候选轨迹的离散空间上进行搜索。这项工作提出了在网格图和基于射线的观测值之间的香农互信息的可区分近似值，该信息可以在SE（3）传感器姿势的连续空间中进行梯度上升优化。我们基于梯度的配方会导致更具信息性的传感轨迹，同时避免阻塞和碰撞。在2-D和3-D环境中的模拟和现实世界实验中证明了该方法。
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.
