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Title Page

Abstract

Contents

Ⅰ. Introduction 10

Ⅱ. Related Works 13

Ⅲ. Preliminary 15

3.1. LiDAR-based SLAM 15

3.2. Graph SLAM 15

3.3. Iterative Closest Point 16

Ⅳ. Problem Formulation 18

Ⅴ. Algorithm Development 19

5.1. Algorithm Overview 19

5.2. Preprocessing 20

5.3. 1st stage : Coarse Registration 21

5.4. 2nd stage : Fine Registration 24

5.5. Rationale for 2-stage model 27

Ⅵ. Experiment 29

6.1. Experiment Setup 29

6.2. Results 30

Ⅶ. Conclusion 33

References 34

List of Figures

Figure 1. Graph SLAM 16

Figure 2. Overall architecture of the proposed method 19

Figure 3. Voxel downsampling 20

Figure 4. (left) Before applying statistical outlier removal; (right) After applying statistical outlier removal 21

Figure 5. Normal scoring 23

Figure 6. Visibility scoring presented in [1] 25

Figure 7. The result of visibility scoring 27

Figure 8. (a) whole trajectory of KITTI dataset, (b) example scene 29

Figure 9. Experiment result compared to ICP, Point-to-Plane ICP and NDT 30

Figure 10. Sample scene in dataset 31

Figure 11. Sample scene in dataset having initial error 31

Figure 12. Comparison result of ICP and proposed method 32

초록보기

 Simultaneous localization and mapping (SLAM) utilizes consecutive sensor inputs to compute its movement over time. During this process, errors continue to accumulate at each step. These accumulated errors are a significant challenge for SLAM. The most well-known solution for these accumulated errors is loop closing. Lidar-based SLAM closes the loop by registering two point clouds. However the point cloud registration is easy to fail when there are missing points between two point clouds due to occlusion and partial overlap. To address this problem, here we proposes a viewpoint-aware point cloud registration method that applies weight in the point-to-plane iterative closest point (ICP) algorithm to consider the visibility of source point cloud from the target point cloud viewpoint. The proposed method yields more accurate and robust results than the baseline approaches in the experiments using KITTI dataset.

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