提出TRIP方法，让四足机器人在线构建更安全的地形可通行地图。
TRIP: Terrain Traversability Mapping With Risk-Aware Prediction for Enhanced Online Quadrupedal Robot Navigation
- 在球面投影空间中估计步态适应性，缓解视角受限导致的数据稀疏问题。
- 基于T-BGK推断法提升地形补全精度与效率，重建更完整的可通行区域。
- 融合马哈拉诺比斯距离增强抗干扰能力，适合复杂动态环境中的自主导航。
在建筑工地和灾害现场等复杂环境中，基于在线稠密地形图的准确可通行性评估对安全导航至关重要。然而，由于视野受限、数据遮挡和稀疏性，腿式机器人在崎岖地形上的可通行性评估面临重大挑战。为此，我们提出风险感知的地形可通行性映射（TRIP）方法。TRIP在重建地形图的同时预测多模态可通行性风险，显著提升在线自主导航能力。首先，在球面投影空间中估计步态适应性，可在应对数据稀疏的同时支持可扩展的地形特性建模；其次，提出的可通行性感知贝叶斯广义核（T-BGK）推断方法提升了地形补全的准确性和效率；最后，利用基于步态适应性的马哈拉诺比斯距离，增强了对异常值和动态元素的鲁棒性，最终生成静态地形可通行性地图。在公开数据集及自建数据集上验证，TRIP在地形重建与导航地图性能上均有显著提升。演示视频展示其作为四足机器人机载在线自主导航系统核心组件的可行性，见 https://youtu.be/d7HlqAP4l0c。
原文摘要 · Abstract (English)
Accurate traversability estimation using an online dense terrain map is crucial for safe navigation in challenging environments like construction and disaster areas. However, traversability estimation for legged robots on rough terrains faces substantial challenges owing to limited terrain information caused by restricted field-of-view, and data occlusion and sparsity. To robustly map traversable regions, we introduce terrain traversability mapping with risk-aware prediction (TRIP). TRIP reconstructs the terrain maps while predicting multi-modal traversability risks, enhancing online autonomous navigation with the following contributions. Firstly, estimating steppability in a spherical projection space allows for addressing data sparsity while accomodating scalable terrain properties. Moreover, the proposed traversability-aware Bayesian generalized kernel (T-BGK)-based inference method enhances terrain completion accuracy and efficiency. Lastly, leveraging the steppability-based Mahalanobis distance contributes to robustness against outliers and dynamic elements, ultimately yielding a static terrain traversability map. As verified in both public and our in-house datasets, our TRIP shows significant performance increases in terms of terrain reconstruction and navigation map. A demo video that demonstrates its feasibility as an integral component within an onboard online autonomous navigation system for quadruped robots is available at https://youtu.be/d7HlqAP4l0c.
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