BALM: Bundle Adjustment for Lidar Mapping

Liu, Zheng; Zhang, Fu

doi:10.1109/lra.2021.3062815

Cited by 147 publications

(93 citation statements)

References 40 publications

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“…In addition to automatic point cloud alignment between consecutive poses, globally optimized and consistent alignment approaches such as (Liu and Zhang, 2021), (Gojcic et al 2020) and (Theiler, Wegner and Schindler 2015) that takes all poses and point clouds in account and align them as a whole with constraints can be implemented to produce an accurate final point cloud. However, in the current state of the development, fine alignment using CloudCompare ICP tool is manually applied in the post processing stage.…”

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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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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“…In GPS denied environment (Section VI-B and Section VI-C), we use the ArUco marker [25] for calculating the drift of our odometry. Notice that all of the mechanical modules of this device are designed as FDM printable 8 , and to faciliate our reader to reproduce our work, we open source all of our schematics on our github 9 .…”

Section: A Equipment Setupmentioning

confidence: 99%

R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

Lin¹,

Zhang²

2021

Preprint

Self Cite

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In this letter, we propose a novel LiDAR-Inertial-Visual sensor fusion framework termed R 3 LIVE, which takes advantage of measurement of LiDAR, inertial, and visual sensors to achieve robust and accurate state estimation. R 3 LIVE is contained of two subsystems, the LiDAR-inertial odometry (LIO) and visual-inertial odometry (VIO). The LIO subsystem (FAST-LIO) takes advantage of the measurement from LiDAR and inertial sensors and builds the geometry structure of (i.e. the position of 3D points) global maps. The VIO subsystem utilizes the data of visual-inertial sensors and renders the map's texture (i.e. the color of 3D points). More specifically, the VIO subsystem fuses the visual data directly and effectively by minimizing the frame-to-map photometric error. The developed system R 3 LIVE is developed based on our previous work R 2 LIVE, with careful architecture design and implementation. Experiment results show that the resultant system achieves more robustness and higher accuracy in state estimation than current counterparts (see our attached video 1 ). R 3 LIVE is a versatile and well-engineered system toward various possible applications, which can not only serve as a SLAM system for real-time robotic applications, but can also reconstruct the dense, precise, RGB-colored 3D maps for applications like surveying and mapping. Moreover, to make R 3 LIVE more extensible, we develop a series of offline utilities for reconstructing and texturing meshes, which further minimizes the gap between R 3 LIVE and various of 3D applications such as simulators, video games and etc (see our demos video 2 ). To share our findings and make contributions to the community, we open source R 3 LIVE on our Github 3 , including all of our codes, software utilities, and the mechanical design of our device.

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“…Grant et al [25] proposed a new planar feature extraction method for fast point cloud registration. When faced with a large number of dense clouds, one can apply bundle adjustment to lidar SLAM to handle large-scale edge and plane features [26] or use sliding windows to increase the speed of the system [27].…”

Section: Related Workmentioning

confidence: 99%

Real-Time 2-D Lidar Odometry Based on ICP

Liu

Zhao

et al. 2021

Sensors

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This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.

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BALM: Bundle Adjustment for Lidar Mapping

Cited by 147 publications

References 40 publications

Open Source and Affordable Terrestrial Laser Scanner

Open Source and Affordable Terrestrial Laser Scanner

R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

Real-Time 2-D Lidar Odometry Based on ICP

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