3D事前地図上でのスライディングウィンドウ因子グラフ最適化に基づく密結合レンジ慣性ローカリゼーション
Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization
スキャン間・スキャン地図間の点群レジストレーションとIMUをスライディングウィンドウ因子グラフで密結合し、点群劣化や地図欠損領域でも頑健に自己位置推定する手法を提案。
著者: Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno
分類: cs.RO
原文アブストラクト
This paper presents a range inertial localization algorithm for a 3D prior map. The proposed algorithm tightly couples scan-to-scan and scan-to-map point cloud registration factors along with IMU factors on a sliding window factor graph. The tight coupling of the scan-to-scan and scan-to-map registration factors enables a smooth fusion of sensor ego-motion estimation and map-based trajectory correction that results in robust tracking of the sensor pose under severe point cloud degeneration and defective regions in a map. We also propose an initial sensor state estimation algorithm that robustly estimates the gravity direction and IMU state and helps perform global localization in 3- or 4-DoF for system initialization without prior position information. Experimental results show that the proposed method outperforms existing state-of-the-art methods in extremely severe situations where the point cloud data becomes degenerate, there are momentary sensor interruptions, or the sensor moves along the map boundary or into unmapped regions.
