MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation


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.


MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation.
Jing Liang, Peng Gao, Xuesu Xiao, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Ming C. Lin and Dinesh Manocha



The GitHub is here.