2 research outputs found
Real-Time, Environmentally-Robust 3D LiDAR Localization
Localization, or position fixing, is an important problem in robotics
research. In this paper, we propose a novel approach for long-term localization
in a changing environment using 3D LiDAR. We first create the map of a real
environment using GPS and LiDAR. Then, we divide the map into several small
parts as the targets for cloud registration, which can not only improve the
robustness but also reduce the registration time. PointLocalization allows us
to fuse different kinds of odometers, which can optimize the accuracy and
frequency of localization results. We evaluate our algorithm on an unmanned
ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the
localization results at more than 20 Hz after fusion. The algorithm can also
localize the UGV in a 180-degree field of view (FOV). Using an outdated map
captured six months ago, this algorithm shows great robustness, and the test
results show that it can achieve an accuracy of 10 cm. PointLocalization has
been tested for a period of more than six months in a crowded factory and has
operated successfully over a distance of more than 2000 km.Comment: 6 pages, 8 figures, 2019 IEEE International Conference on Imaging
Systems and Techniques (IST
Automatic Calibration of Dual-LiDARs Using Two Poles Stickered with Retro-Reflective Tape
Multi-LiDAR systems have been prevalently applied in modern autonomous
vehicles to render a broad view of the environments. The rapid development of
5G wireless technologies has brought a breakthrough for current cellular
vehicle-to-everything (C-V2X) applications. Therefore, a novel localization and
perception system in which multiple LiDARs are mounted around cities for
autonomous vehicles has been proposed. However, the existing calibration
methods require specific hard-to-move markers, ego-motion, or good initial
values given by users. In this paper, we present a novel approach that enables
automatic multi-LiDAR calibration using two poles stickered with
retro-reflective tape. This method does not depend on prior environmental
information, initial values of the extrinsic parameters, or movable platforms
like a car. We analyze the LiDAR-pole model, verify the feasibility of the
algorithm through simulation data, and present a simple method to measure the
calibration errors w.r.t the ground truth. Experimental results demonstrate
that our approach gains better flexibility and higher accuracy when compared
with the state-of-the-art approach.Comment: 6 pages, 7 figures, 2019 IEEE Conference on Imaging Systems and
Techniques (IST