Modeling Point Uncertainty in Radar SLAM

While visual and laser-based simultaneous localization and mapping (SLAM) techniques have gained significant attention, radar SLAM remains a robust option for challenging conditions. This paper aims to improve the performance of radar SLAM by modeling point uncertainty. The basic SLAM system is a radar-inertial odometry (RIO) system that leverages velocity-aided radar points and high-frequency inertial measurements. We first propose to model the uncertainty of radar points in polar coordinates by considering the nature of radar sensing. Then in the SLAM system, the uncertainty model is designed into the data association module and is incorporated to weight the motion estimation. Real-world experiments on public and self-collected datasets validate the effectiveness of the proposed models and approaches. The findings highlight the potential of incorporating radar point uncertainty modeling to improve the radar SLAM system in adverse environments.

Less is More: Physical-enhanced Radar-Inertial Odometry

Radar offers the advantage of providing additional physical properties related to observed objects. In this study, we design a physical-enhanced radar-inertial odometry system that capitalizes on the Doppler velocities and radar cross-section information. The filter for static radar points, correspondence estimation, and residual functions are all strengthened by integrating the physical properties. We conduct experiments on both public datasets and our self-collected data, with different mobile platforms and sensor types. Our quantitative results demonstrate that the proposed radar-inertial odometry system outperforms alternative methods using the physical-enhanced components. Our findings also reveal that using the physical properties results in fewer radar points for odometry estimation, but the performance is still guaranteed and even improved, thus aligning with the ``less is more'' principle.