Whisker-like touch sensors offer unique advantages for short-range perception in environments where visual and long-range sensing are unreliable, such as confined, cluttered, or low-visibility settings. This paper presents a framework for estimating contact points and robot localization in a known planar environment using a single whisker sensor. We develop a family of virtual sensor models. Each model maps robot configurations to sensor observations and enables structured reasoning through the concept of preimages - the set of robot states consistent with a given observation. The notion of virtual sensor models serves as an abstraction to reason about state uncertainty without dependence on physical implementation. By combining sensor observations with a motion model, we estimate the contact point. Iterative estimation then enables reconstruction of obstacle boundaries. Furthermore, intersecting states inferred from current observations with forward-projected states from previous steps allow accurate robot localization without relying on vision or external systems. The framework supports both deterministic and possibilistic formulations and is validated through simulation and physical experiments using a low-cost, 3D printed, Hall-effect-based whisker sensor. Results demonstrate accurate contact estimation and localization with errors under 7 mm, demonstrating the potential of whisker-based sensing as a lightweight, adaptable complement to vision-based navigation.

翻译：胡须状触觉传感器在视觉和远距离感知不可靠的环境（如狭窄、杂乱或低能见度场景）中，为短距离感知提供了独特优势。本文提出了一种利用单个胡须传感器在已知平面环境中估计接触点并进行机器人定位的框架。我们开发了一系列虚拟传感器模型。每个模型将机器人构型映射到传感器观测值，并通过**原像**（即与给定观测值一致的机器人状态集合）这一概念实现结构化推理。虚拟传感器模型的概念作为一种抽象，用于推理状态不确定性，而不依赖于物理实现。通过将传感器观测与运动模型相结合，我们估计出接触点。随后，通过迭代估计实现障碍物边界的重建。此外，将当前观测推断的状态与先前步骤前向投影的状态相交，可在不依赖视觉或外部系统的情况下实现精确的机器人定位。该框架支持确定性和可能性两种表述形式，并通过使用低成本、3D打印、基于霍尔效应的胡须传感器进行了仿真和物理实验验证。结果表明，接触点估计与定位误差小于7毫米，证明了基于胡须的传感作为视觉导航的一种轻量级、适应性强的补充手段具有巨大潜力。