Although haptic sensing has recently been used for legged robot localization in extreme environments where a camera or LiDAR might fail, the problem of efficiently representing the haptic signatures in a learned prior map is still open. This paper introduces an approach to terrain representation for haptic localization inspired by recent trends in machine learning. It combines this approach with the proven Monte Carlo algorithm to obtain an accurate, computation-efficient, and practical method for localizing legged robots under adversarial environmental conditions. We apply the triplet loss concept to learn highly descriptive embeddings in a transformer-based neural network. As the training haptic data are not labeled, the positive and negative examples are discriminated by their geometric locations discovered while training. We demonstrate experimentally that the proposed approach outperforms by a large margin the previous solutions to haptic localization of legged robots concerning the accuracy, inference time, and the amount of data stored in the map. As far as we know, this is the first approach that completely removes the need to use a dense terrain map for accurate haptic localization, thus paving the way to practical applications.

翻译：尽管触觉感知最近被用于在摄像头或激光雷达可能失效的极端环境中进行足式机器人定位，但如何有效表征学习先验地图中的触觉特征仍是一个开放问题。本文受机器学习最新趋势启发，提出了一种用于触觉定位的地形表征方法。该方法与经过验证的蒙特卡洛算法相结合，获得了一种在恶劣环境条件下精确、计算高效且实用的足式机器人定位方法。我们应用三元组损失概念，在基于Transformer的神经网络中学习高描述性嵌入。由于训练触觉数据未经标注，正负样本通过训练过程中发现的几何位置进行区分。实验表明，所提方法在定位精度、推理时间及地图存储数据量方面，显著优于先前的足式机器人触觉定位方案。据我们所知，这是首个完全无需稠密地形地图即可实现精确触觉定位的方法，从而为实际应用铺平了道路。