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

TL;DR

This paper introduces a fast, reactive obstacle avoidance method for MAVs using Riemannian Motion Policies with GPU raycasting on voxel maps, enabling real-time navigation without complex pre-processing.

Contribution

It presents a novel GPU-based raycasting approach to apply Riemannian Motion Policies directly on voxel maps and LiDAR scans, improving efficiency and flexibility for MAV obstacle avoidance.

Findings

Achieves kilohertz-rate obstacle avoidance using thousands of rays

Demonstrates successful real-time navigation on a MAV with static and dynamic obstacles

Provides an open-source implementation for the robotics community

Abstract

In this paper, we present a novel method for using Riemannian Motion Policies on volumetric maps, shown in the example of obstacle avoidance for Micro Aerial Vehicles (MAVs). While sampling or optimization-based planners are widely used for obstacle avoidance with volumetric maps, they are computationally expensive and often have inflexible monolithic architectures. Riemannian Motion Policies are a modular, parallelizable, and efficient navigation paradigm but are challenging to use with the widely used voxel-based environment representations. We propose using GPU raycasting and a large number of concurrent policies to provide direct obstacle avoidance using Riemannian Motion Policies in voxelized maps without the need for smoothing or pre-processing of the map. Additionally, we present how the same method can directly plan on LiDAR scans without the need for an intermediate map. We show how this reactive approach compares favorably to traditional planning methods and is able to plan using thousands of rays at kilohertz rates. We demonstrate the planner successfully on a real MAV for static and dynamic obstacles. The presented planner is made available as an open-source software package.