MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation

TL;DR

This paper introduces a learning-based, mapless trajectory generator for outdoor robot navigation that ensures comprehensive traversability coverage and collision avoidance using limited perception.

Contribution

It presents a novel CVAE-based trajectory generation method with traversability constraints for global outdoor navigation without relying on maps.

Findings

6% improvement in traversability coverage

89% reduction in non-traversable trajectory segments

Effective in complex outdoor environments

Abstract

We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global planning using limited onboard robot perception in mapless environments while ensuring comprehensive coverage of all traversable directions. Our formulation uses a Conditional Variational Autoencoder (CVAE) generative model that is enhanced with traversability constraints and an optimization formulation used for the coverage. We highlight the benefits of our approach over state-of-the-art trajectory generation approaches and demonstrate its performance in challenging and large outdoor environments, including around buildings, across intersections, along trails, and off-road terrain, using a Clearpath Husky and a Boston Dynamics Spot robot. In practice, our approach results in a 6% improvement in coverage of traversable areas and an 89% reduction in trajectory portions residing in non-traversable regions. Our video is here: https://youtu.be/3eJ2soAzXnU