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The problem of active mapping aims to plan an informative sequence of sensingviews given a limited budget such as distance traveled. This paper consideractive occupancy grid mapping using a range sensor, such as LiDAR or depthcamera. State-of-the-art methods optimize information-theoretic measuresrelating the occupancy grid probabilities with the range sensor measurements.The non-smooth nature of ray-tracing within a grid representation makes theobjective function non-differentiable, forcing existing methods to search overa discrete space of candidate trajectories. This work proposes a differentiableapproximation of the Shannon mutual information between a grid map andray-based observations that enables gradient ascent optimization in thecontinuous space of SE(3) sensor poses. Our gradient-based formulation leads tomore informative sensing trajectories, while avoiding occlusions andcollisions. The proposed method is demonstrated in simulated and real-worldexperiments in 2-D and 3-D environments.

Active Mapping via Gradient Ascent Optimization of Shannon Mutual Information over Continuous SE(3) Trajectories