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

2025-01-12

AI Summary

Accurate terrain traversability estimation for robots operating in unstructured outdoor environments—such as forests—remains challenging due to geometric complexity and dynamic motion constraints. Method: This paper proposes a heading-aware, real-time, continuous traversability estimation framework. Departing from conventional static discriminative models, it jointly encodes robot ego-motion (recent pose change sequences) and high-fidelity geometric observations (LiDAR point clouds or depth maps) within a multimodal deep neural network. Critically, the model is trained exclusively in simulation and achieves zero-shot transfer to real-world deployment without domain adaptation or real-world annotations. Results: Extensive experiments demonstrate state-of-the-art performance both in simulation and on physical robotic platforms. The method enables robust path planning and active exploration over complex, uneven terrain while eliminating the need for costly real-world labeled data.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• terrain navigability
• outdoor environments
• robot orientation influence

Innovation

• Intelligent System
• Direction-aware Navigation
• Simulation to Reality Transfer