Abstract
When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.
Highlights
In recent years, autonomous navigation technology has been gradually deployed on outdoor vehicles operated in unstructured terrain
We propose a two-step registration method consisting of initial registration and accurate registration
This paper proposes a two-step registration method for fast point cloud reconstruction of unstructured terrains
Summary
Autonomous navigation technology has been gradually deployed on outdoor vehicles operated in unstructured terrain. Compared with visual image sensors, LIDAR has the advantage of being free from the effect of light and weather and can obtain the three-dimensional terrain information conveniently It has been more and more widely used in autonomous vehicles for navigation purpose. Feature description can effectively conduct the registration of point clouds with uneven-density and high-noise and improve the performance of the traditional ICP algorithm. Zhang et al [24] extracted key points based on curvature features without full consideration of the neighborhood of the point, which bring about many feature-similar points when used for the registration of complicated point clouds The limitations of these methods make them difficult to be used for the fast point cloud reconstruction of unstructured terrains, a new feature descriptor is required.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
