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In this paper, we present a novel method for using Riemannian Motion Policieson volumetric maps, shown in the example of obstacle avoidance for Micro AerialVehicles (MAVs). While sampling or optimization-based planners are widely usedfor obstacle avoidance with volumetric maps, they are computationally expensiveand often have inflexible monolithic architectures. Riemannian Motion Policiesare a modular, parallelizable, and efficient navigation paradigm but arechallenging to use with the widely used voxel-based environmentrepresentations. We propose using GPU raycasting and a large number ofconcurrent policies to provide direct obstacle avoidance using RiemannianMotion Policies in voxelized maps without the need for smoothing orpre-processing of the map. Additionally, we present how the same method candirectly plan on LiDAR scans without the need for an intermediate map. We showhow this reactive approach compares favorably to traditional planning methodsand is able to plan using thousands of rays at kilohertz rates. We demonstratethe planner successfully on a real MAV for static and dynamic obstacles. Thepresented planner is made available as an open-source software package.

Obstacle avoidance using raycasting and Riemannian Motion Policies at kHz rates for MAVs