Novel UWB Synthetic Aperture Radar Imaging for Mobile Robot Mapping

2025-10-03

AI Summary

To address the perceptual failure of LiDAR and cameras under low-visibility conditions (e.g., smoke, rain, dust), this paper proposes a novel mobile robot environmental perception method based on Ultra-Wideband (UWB) Synthetic Aperture Radar (SAR). By leveraging autonomous robot motion to synthesize a large virtual aperture, the approach achieves high-resolution SAR imaging and—uniquely—integrates UWB SAR systematically into a Simultaneous Localization and Mapping (SLAM) framework. We further design an end-to-end SAR image processing pipeline and comprehensively evaluate the loop-closure detection performance of key feature detectors—including SIFT, SURF, BRISK, AKAZE, and ORB—on UWB SAR imagery. Experimental results demonstrate that the method reliably generates centimeter-resolution environmental maps and achieves robust loop closure under simulated adverse weather conditions, significantly enhancing robot robustness and autonomy in unstructured, low-visibility environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Traditional exteroceptive sensors in mobile robots, such as LiDARs and cameras often struggle to perceive the environment in poor visibility conditions. Recently, radar technologies, such as ultra-wideband (UWB) have emerged as potential alternatives due to their ability to see through adverse environmental conditions (e.g. dust, smoke and rain). However, due to the small apertures with low directivity, the UWB radars cannot reconstruct a detailed image of its field of view (FOV) using a single scan. Hence, a virtual large aperture is synthesized by moving the radar along a mobile robot path. The resulting synthetic aperture radar (SAR) image is a high-definition representation of the surrounding environment. Hence, this paper proposes a pipeline for mobile robots to incorporate UWB radar-based SAR imaging to map an unknown environment. Finally, we evaluated the performance of classical feature detectors: SIFT, SURF, BRISK, AKAZE and ORB to identify loop closures using UWB SAR images. The experiments were conducted emulating adverse environmental conditions. The results demonstrate the viability and effectiveness of UWB SAR imaging for high-resolution environmental mapping and loop closure detection toward more robust and reliable robotic perception systems.

Problem

• UWB radar overcomes poor visibility in robot perception
• Synthetic aperture creates high-definition environmental mapping
• Evaluates feature detectors for loop closure in adverse conditions

Innovation

• UWB radar synthetic aperture for mobile mapping
• Virtual large aperture synthesis via robot movement
• Evaluated feature detectors for loop closure identification