Radar-Inertial SLAM Dataset
Morten Nissov Nikhil Khedekar Kostas Alexis
Autonomous Robots Lab, Norwegian University of Science and Technology
Abstract: A plethora of localization solutions for different sensor modalities exists, yet robust sensing in degraded perception conditions remains an open challenge. For example, the perception sensors most typically used in mobile robotics, like visible-light cameras and LiDARs, can face significant problems when operating in environments with dense obscurants (e.g., fog, smoke or dust). However, millimeter wave radars offer an avenue to penetrate such conditions and overcome these limitations. Motivated by the above, in this paper we propose a new radar-inertial SLAM method that utilizes doppler velocity-based radar-inertial odometry combined with aggregate radar point cloud registration on both short- and long-term (loop closure) associations. Experimental evaluations are conducted on both publicly available and self-collected datasets and allow to demonstrate the performance of the method.
The dataset can currently being downloaded via a shared drive link. A more detailed sharing solution will be provided soon.