Active Mapping via Gradient Ascent Optimization of Shannon Mutual Information over Continuous SE(3) Trajectories
Active Mapping via Gradient Ascent Optimization of Shannon Mutual Information over Continuous SE(3) Trajectories
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DOI：
10.1109/iros47612.2022.9981875
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Arash Asgharivaskasi;Shumon Koga;Nikolay A. Atanasov
中科院分区：
文献类型：
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作者：
Arash Asgharivaskasi;Shumon Koga;Nikolay A. Atanasov
主动映射的问题的目的是计划一个信息序列的传感视图给定有限的预算，如行驶的距离。本文考虑使用范围传感器（如LiDAR或深度相机）进行主动占用网格映射。国家的最先进的方法优化信息理论的措施有关的占用网格概率与范围传感器测量。网格表示内的光线跟踪的非平滑性质使得目标函数不可微，迫使现有方法在候选轨迹的离散空间上搜索。这项工作提出了一种网格地图和基于射线的观测之间的香农互信息的可微近似，该近似使得能够在SE（3）传感器姿态的连续空间中进行梯度上升优化。我们的基于梯度的配方导致更多的信息感测轨迹，同时避免遮挡和碰撞。所提出的方法在2-D和3-D环境中的模拟和真实世界的实验证明。本文的补充材料可在以下网址获得：https://arashasgharivaskasi-bc.github.io/grad_active_mapping/
The problem of active mapping aims to plan an informative sequence of sensing views given a limited budget such as distance traveled. This paper considers 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. Materials supplementing this paper are available at: https://arashasgharivaskasi-bc.github.io/grad_active_mapping/