Intensity-Assisted ICP for Fast Registration of 2D-LIDAR.

Yingzhong Tian,Long Li,Xining Liu,Wenbin Wang

doi:10.3390/s19092124

Yingzhong Tian, Long Li + Show 2 more

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https://doi.org/10.3390/s19092124

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Abstract

Iterative closest point (ICP) is a method commonly used to perform scan-matching and registration. To be a simple and robust algorithm, it is still computationally expensive, and it has been regarded as having a crucial challenge especially in a real-time application as used for the simultaneous localization and mapping (SLAM) problem. For these reasons, this paper presents a new method for the acceleration of ICP with an assisted intensity. Unlike the conventional ICP, this method is proposed to reduce the computational cost and avoid divergences. An initial transformation guess is computed with an assisted intensity for their relative rigid-body transformation. Moreover, a target function is proposed to determine the best initial transformation guess based on the statistic of their spatial distances and intensity residuals. Additionally, this method is also proposed to reduce the iteration number. The Anderson acceleration is utilized for increasing the iteration speed which has better ability than the Picard iteration procedure. The proposed algorithm is operated in real time with a single core central processing unit (CPU) thread. Hence, it is suitable for the robot which has limited computation resources. To validate the novelty, this proposed method is evaluated on the SEMANTIC3D.NET benchmark dataset. According to comparative results, the proposed method is declared as having better accuracy and robustness than the conventional ICP methods.

Highlights

The estimation step carried out simultaneously to maintain the robot pose is regarded as the main work of simultaneous localization and mapping (SLAM) [1]
A novel modification of the conventional iterative closest point (ICP) based on the initial transformation guess and Anderson acceleration indicates the proposed method can perform well in this paper
A target function is proposed to determine the best initial transformation guess based on the statistic of their spatial distances and intensity residuals with a little runtime cost

Summary

Introduction

The estimation step carried out simultaneously to maintain the robot pose is regarded as the main work of simultaneous localization and mapping (SLAM) [1]. The iterative closest point (ICP) [2] is a well-known algorithm to deal with the laser scanner data presented in the form of point clouds. A vehicle localization method based on a vertical corner feature used ICP algorithm to deal with the geometric relations between the scan data of the 3D light detection and ranging (LIDAR) [4]. A technique to estimate the ego motion of a vehicle was presented in [5] where the ICP algorithms were evaluated for their accuracy and computational speed based on laser range data. A 3D mapping system proposed an art ICP-based SLAM method [6] using only point cloud data

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