A Hough transform approach to safety-aware scalar field mapping using Gaussian Processes

2026-04-22

AI Summary

This work addresses the problem of safe scalar field mapping in unknown environments containing high-risk regions by proposing an active exploration method that integrates Gaussian process regression with the Hough transform. For the first time, the Hough transform is incorporated into the posterior analysis of Gaussian processes to enable real-time identification of hazardous regions’ geometric structures. The approach guides robotic sampling toward informative yet provably safe locations under probabilistic safety constraints. By synergistically combining Bayesian inference with safe motion planning, the proposed strategy simultaneously ensures collision avoidance and optimizes information gain. The efficacy of the method is validated through two sets of simulations and a real-world indoor experiment mapping a light intensity field, demonstrating its superior performance in both safety assurance and mapping accuracy.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper presents a framework for mapping unknown scalar fields using a sensor-equipped autonomous robot operating in unsafe environments. The unsafe regions are defined as regions of high-intensity, where the field value exceeds a predefined safety threshold. For safe and efficient mapping of the scalar field, the sensor-equipped robot must avoid high-intensity regions during the measurement process. In this paper, the scalar field is modeled as a sample from a Gaussian process (GP), which enables Bayesian inference and provides closed-form expressions for both the predictive mean and the uncertainty. Concurrently, the spatial structure of the high-intensity regions is estimated in real-time using the Hough transform (HT), leveraging the evolving GP posterior. A safe sampling strategy is then employed to guide the robot towards safe measurement locations, using probabilistic safety guarantees on the evolving GP posterior. The estimated high-intensity regions also facilitate the design of safe motion plans for the robot. The effectiveness of the approach is verified through two numerical simulation studies and an indoor experiment for mapping a light-intensity field using a wheeled mobile robot.

Problem

• scalar field mapping
• safety-aware navigation
• unsafe environments
• autonomous robot
• Gaussian Processes

Innovation

• Gaussian Processes
• Hough Transform
• Safety-aware Mapping
• Autonomous Robotics
• Scalar Field Estimation