METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation

TL;DR

This paper presents METAVerse, a meta-learning framework for learning a global model that accurately and reliably predicts terrain traversability across diverse environments and demonstrates that the reduced uncertainty results in safe and stable navigation in unstructured and unknown terrains.

Abstract

Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can accurately predict traversability in a variety of environments. This paper presents METAVerse, a meta-learning framework for learning a global model that accurately and reliably predicts terrain traversability across diverse environments. We train the traversability prediction network to generate a dense and continuous-valued cost map from a sparse LiDAR point cloud, leveraging vehicle-terrain interaction feedback in a self-supervised manner. Meta-learning is utilized to train a global model with driving data collected from multiple environments, effectively minimizing estimation uncertainty. During deployment, online adaptation is performed to rapidly adapt the network to the local environment by exploiting recent interaction experiences. To conduct a comprehensive evaluation, we collect driving data from various terrains and demonstrate that our method can obtain a global model that minimizes uncertainty. Moreover, by integrating our model with a model predictive controller, we demonstrate that the reduced uncertainty results in safe and stable navigation in unstructured and unknown terrains.

Figures (5)

• Figure 1: Overview pipeline of the traversability cost prediction network. From a single sweep LiDAR point cloud, the network generates a dense and continuous-valued traversability cost map in BEV.
• Figure 2: Real-world driving data. Based on the terrain characteristics, the evaluation data is divided into three distinct categories: (a) Unpaved, (b) Grassland, and (c) Profiled Road. RGB images and the traversability cost map generated by LiDAR points of these scenes are displayed. More results are available in the multimedia material.
• Figure 3: (a) Mean square error for the evaluation dataset. The error decreases as the number of adaptation steps increases. Our model reduces prediction uncertainty significantly compared to a model trained without a meta-objective (Baseline). (b) The initially inaccurate cost map is updated through adaptation, incorporating the recent experience of vehicles traversing similar bumps and puddles to predict the traversability cost more accurately.
• Figure 4: (Left) The off-road race track for the navigation experiment (Q2). (Right) Vehicle trajectories taken by employing various traversability maps. We observe that METAVerse can navigate through relatively safer trajectories by accurately identifying nuanced traversability.
• Figure 5: (a) The off-road race track designed for online adaptation (Q3). The vehicle trajectories are visualized, and the colors of the lines illustrate the rotational impacts exerted on the vehicle. (b) Vertical acceleration of the vehicle during navigation. By conducting online adaptation, the vehicle can plan paths that can minimize impacts exerted on the vehicle, leading to stable navigation in off-road.