Novel UWB Synthetic Aperture Radar Imaging for Mobile Robot Mapping

TL;DR

This work addresses mapping in vision-denied environments by leveraging ultra-wideband radar for synthetic-aperture SAR imaging, formed by moving the radar on a mobile robot. It presents a complete pipeline from UWB SAR data collection and back-projection reconstruction to evaluating classical visual features (SIFT, SURF, BRISK, AKAZE, ORB) for loop-closure detection, validated with indoor experiments and LiDAR ground truth. The study finds AKAZE and ORB offer the most reliable feature matches on SAR images, and demonstrates a robust loop-closure strategy that fuses both detectors' estimates. The authors also publicize their datasets and ROS2-based code, highlighting practical implications for robust perception in adverse conditions and future UWB SAR-based SLAM development.

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.

Figures (14)

• Figure 1: This paper analyses the feasibility of utilizing visual feature detectors on UWB SAR images to identify loop closures when a mobile robot explores in a vision-denied environment (e.g. smoke-filled setting).
• Figure 2: The plan view of the UWB radars mounted on a non-holonomic robot. The sensors are oriented perpendicular to the moving direction.
• Figure 3: Transmitted Gaussian pulse. The specifications are given in Table \ref{['tab1']}. Recreated using scipy.signal.gausspulse.
• Figure 4: Overview of the back-projection algorithm. The SAR image is reconstructed by coherently summing the received radar signals along the time-delay paths corresponding to each image pixel.
• Figure 5: An illustration of the back-projection algorithm from a programming perspective. The grid (left) represents pixels of the SAR image. All coordinates are calculated w.r.t. the image coordinate frame $I$.