Abstract
Multi-sensor fusion-based localization technology has achieved high
accuracy in autonomous systems. How to improve the robustness is the
main challenge at present. The most commonly used LiDAR and camera are
weather-sensitive, while the FMCW Radar has strong adaptability but
suffers from noise and ghost effects. In this paper, we propose a
heterogeneous localization method called Radar on LiDAR Map (RoLM),
which aims to enhance localization accuracy without relying on loop
closures by mitigating the accumulated error in Radar odometry in real
time. Our approach involves embedding the data from both Radar and LiDAR
sensors into a density map. We calculate the spatial vector similarity
with an offset to determine the corresponding place index within the
candidate map and estimate the rotation and translation. To refine the
alignment, we utilize the Iterative Closest Point (ICP) algorithm to
achieve optimal matching on the LiDAR submap. We conducted extensive
experiments on the Mulran Radar Dataset, Oxford Radar RobotCar Dataset,
and our dataset to demonstrate the feasibility and effectiveness of our
proposed approach.