Voxelized GICP for Fast and Accurate 3D Point Cloud Registration

Koide, Kenji; Yokozuka, Masashi; Oishi, Shuji; Banno, Atsuhiko

doi:10.1109/icra48506.2021.9560835

Cited by 199 publications

(82 citation statements)

References 34 publications

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“…This ultimately assumes that the environment is smooth and hence can be viewed as a plane locally. However, the computation load of generalized-ICP is usually large [33]. Other works based on Normal Distribution Transformation (NDT) [34]- [36] also register raw points, but NDT has lower stability compared to ICP and may diverge in some scenes [36].…”

Section: A Lidar(-inertial) Odometrymentioning

confidence: 99%

FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

et al. 2022

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This paper presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly-coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and hence increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adaptable to emerging LiDARs of different scanning patterns; The second main novelty is maintaining a map by an incremental k-d tree data structure, ikd-Tree, that enables incremental updates (i.e., point insertion, delete) and dynamic re-balancing. Compared with existing dynamic data structures (octree, R * -tree, nanoflann k-d tree), ikd-Tree achieves superior overall performance while naturally supports downsampling on the tree. We conduct an exhaustive benchmark comparison in 19 sequences from a variety of open LiDAR datasets. FAST-LIO2 achieves consistently higher accuracy at a much lower computation load than other state-of-the-art LiDAR-inertial navigation systems. Various realworld experiments on solid-state LiDARs with small FoV are also conducted. Overall, FAST-LIO2 is computationally-efficient (e.g., up to 100 Hz odometry and mapping in large outdoor environments), robust (e.g., reliable pose estimation in cluttered indoor environments with rotation up to 1000 deg/s), versatile (i.e., applicable to both multi-line spinning and solid-state Li-DARs, UAV and handheld platforms, and Intel and ARM-based processors), while still achieving higher accuracy than existing methods. Our implementation of the system FAST-LIO2, and the data structure ikd-Tree are both open-sourced on Github 2,3 .

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Section: A Lidar(-inertial) Odometrymentioning

confidence: 99%

FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

et al. 2022

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“…We use the Voxelized Generalized Iterative Closest Point (VGICP) in [28]. This voxelized version of GICP aggregates the voxel distribution on each point, parallelizing the optimization and achieving similar accuracy to GICP but substantially faster (it runs at 30 Hz on a low-end CPU).…”

Section: Robot Odometrymentioning

confidence: 99%

Situational Graphs for Robot Navigation in Structured Indoor Environments

Bavle¹,

Sánchez-López²,

Shaheer³

et al. 2022

Preprint

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Autonomous mobile robots should be aware of their situation, understood as a comprehensive understanding of the environment along with the estimation of its own state, to successfully make decisions and execute tasks in natural environments. 3D scene graphs are an emerging field of research with great potential to represent these situations in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been utilized for this, further research is still required to effectively deploy them on-board mobile robots.To this end, we present in this paper a real-time online built Situational Graphs (S-Graphs), composed of a single graph representing the environment, while simultaneously improving the robot pose estimation. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes a robot tracking layer where the robot poses are registered, a metric-semantic layer with features such as planar walls and our novel topological layer constraining higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantictopological model of the environment.

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“…Multiple approaches and implementations for this exist. For example, (Segal, Haehnel and Thrun 2009) for generalized ICP (GICP), (Koide et al 2021) for voxelized GICP approach with GPU based implementation and (Pan et al 2018) for global optimal matching and hybrid metric based approach. Other approaches also exist, each with its own robustness, performance and time trade-offs with respect to processing computer hardware and the nature of the scanned environment.…”

Section: Automatic Point Cloud Alignmentmentioning

confidence: 99%

Open Source and Affordable Terrestrial Laser Scanner

Pyae

Lin

Zaw

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper explores the development of an affordable terrestrial laser scanner (TLS) system based on low-cost components and open source software. This paper enumerates components implemented and integrated in both hardware and software aspects. Primarily, the proposed system depends on Mid-40 LiDAR sensor and utilizes Point Cloud Library (PCL), Open Source Computer Vision Library (OpenCV) and Robotic Operating System (ROS) for system operation and data processing. Furthermore, this paper discusses sensor calibration and fusion methods available and implemented in the processing pipeline. The goal of this work is to produce an open source package of software system and hardware designs of a robust version of the TLS system that can be shared and easily reproduced by individuals and communities for their applications.

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Voxelized GICP for Fast and Accurate 3D Point Cloud Registration

Cited by 199 publications

References 34 publications

FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

Situational Graphs for Robot Navigation in Structured Indoor Environments

Open Source and Affordable Terrestrial Laser Scanner

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