From Simulation to Field: Learning Terrain Traversability for Real-World Deployment

TL;DR

This paper presents a deep learning approach that integrates environmental geometry and robot movement data to improve terrain traversability estimation, enabling better autonomous navigation in complex outdoor environments.

Contribution

A novel neural network model that incorporates robot heading and environmental data for real-time traversability estimation trained solely in simulation.

Findings

Outperforms existing methods in simulated and real environments

Accurately predicts traversability without real-world training data

Enhances path planning and exploration in challenging terrains

Abstract

The challenge of traversability estimation is a crucial aspect of autonomous navigation in unstructured outdoor environments such as forests. It involves determining whether certain areas are passable or risky for robots, taking into account factors like terrain irregularities, slopes, and potential obstacles. The majority of current methods for traversability estimation operate on the assumption of an offline computation, overlooking the significant influence of the robot's heading direction on accurate traversability estimates. In this work, we introduce a deep neural network that uses detailed geometric environmental data together with the robot's recent movement characteristics. This fusion enables the generation of robot direction awareness and continuous traversability estimates, essential for enhancing robot autonomy in challenging terrains like dense forests. The efficacy and significance of our approach are underscored by experiments conducted on both simulated and real robotic platforms in various environments, yielding quantitatively superior performance results compared to existing methods. Moreover, we demonstrate that our method, trained exclusively in a high-fidelity simulated setting, can accurately predict traversability in real-world applications without any real data collection. Our experiments showcase the advantages of our method for optimizing path-planning and exploration tasks within difficult outdoor environments, underscoring its practicality for effective, real-world robotic navigation. In the spirit of collaborative advancement, we have made the code implementation available to the public.