Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

Chiang, Kai-Wei; Chiu, Yuting; Srinara, Surachet; Tsai, Meng-Lun

doi:10.1186/s43020-022-00092-0

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…In terms of Equations ( 4) to (7), the ranging accuracy is decided by the rotation matrix and translation matrix between the structured light frame and the camera frame, as well as the coordinates of light strips in the image frame. In the proposed system, the structured light frame is parallel to the camera frame.…”

Section: Ranging Accuracy Analysismentioning

confidence: 99%

See 1 more Smart Citation

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.

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Section: Ranging Accuracy Analysismentioning

confidence: 99%

“…In previous decades, a laser scanning technique was developed to build a 3D environment model with time efficiency and high accuracy [6,7]. Laser scanners measure the distance, horizontal angle and vertical angle of a point to obtain the 3D coordinate of the point.…”

Section: Introductionmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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“…With PVA information generated from INS mechanization and the elastic method-based HD map, it can provide reliable base map for LiDAR point cloud to realize NDT scan to map matching, which can correct the initial PVA guess. With this piece of information, it can gather with GNSS and motion constraints information back to EKF update (Chiang et al, 2023). but it can tell that there are less features for LiDAR odometry in this scenario, especially in the along-track direction.…”

Section: Ins/ Gnss/ Lidar/ Hd Map Integrated Systemmentioning

confidence: 99%

Evaluating Navigation Performance of Elastically Constructed Hd Map With Multi-Sensor Fusion Engine System

Chiu,

Srinara,

Tsai

et al. 2023

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

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Abstract. In response to the rapid development of autonomous vehicles and the increasing demand for HD maps, the conventional mapping processes following HD maps guidelines require significant manpower and time resources. Therefore, we propose flexible procedures and methods for HD maps creation, aiming to reduce cost expenditure by employing diverse source of ground control point, sensor data collection, and mapping algorithm. This approach accelerates the production speed and capability of HD maps. In this study, we select Taiwan's National Highway No. 8 as the trial field for the elastic HD map construction method, and equipped with autonomous vehicle-grade GNSS, IMU, and LiDAR systems.We align the constructed-map data to the global coordinate system, in order to realize the concept of control point cloud map. To assess the assistance and correction capabilities of HD maps in autonomous vehicle navigation systems, we conduct accuracy evaluation through both direct and indirect methods, and analyse the strengths and weaknesses of each approach. The analysis result demonstrates that the elastic method-built HD maps not only meet the mapping accuracy requirements specified in the HD maps verification and validation guidelines, but also assist autonomous vehicles in realizing positioning, navigation, and timing with “where in lane” level (0.5 meter) accuracy.

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“…The registration-based algorithms involve dense point clouds map as the prior map to provide the geometric information of the surrounding environments. For example, in (Chiang et al, 2023), initial position is estimated based on the NDT alignment between the point clouds and dense point clouds map. In (Gui et al, 2022), NDT is implemented to align the LiDAR to a pre-built offline map to estimate the relative pose along the trajectory and the map matching constraints is to be optimized in the factor graph.…”

Section: Related Workmentioning

confidence: 99%

Lidar-Inertial Navigation Based on Map Aided Distance Constraint and Factor Graph Optimization

Ai,

Elhabiby,

Asl Sabbaghian Hokmabadi

et al. 2023

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

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Abstract. The simultaneous localization and mapping (SLAM) is one of the well-developed positioning technology that provides high accuracy and reliability positioning for automatic vehicles and robotics applications. Integrating Light Detection and Ranging (LiDAR) with an Inertial Measurement Unit (IMU) has emerged as a promising technique for achieving stable navigation results in dense urban environments, outperforming vision-based or pure Inertial Navigation System (INS) solutions. However, conventional LiDAR-Inertial SLAM systems often suffer from limited perception of surrounding geometric information, resulting in unexpected and accumulating errors. In this paper, we proposed a LiDAR-Inertial SLAM scheme that utilizes a prior structural information map which is generated from opensource OpenStreetMap (OSM). In contrast to conventional solutions of OSM-aided SLAM approaches, our method extracts the vectorized models of road and building and synthetically generates dense point maps for LiDAR registration. Specifically, a structural map processing module extracts the road models and building models from OSM and generates a structure information map (SIM) with dense point clouds. Secondly, a map aided distance (MD) constraint is calculated by registering selected keyframes and the prior SIM. Finally, a factor graph optimization (FGO) algorithm is involved to integrate the relative transformation obtained from LiDAR odometry, IMU pre-integration, and the map aided distance constraints. To evaluate the proposed LiDAR-based positioning accuracy, experimental evaluation is implemented in an opensource dataset collected in the urban canyon environments. Experimental results demonstrates that with the help of the proposed MD constraint, the LiDAR-based navigation solution can achieve accurate positioning, with a root mean square deviation (RSME) of 4.7 m.

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Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

Cited by 8 publications

References 23 publications

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Evaluating Navigation Performance of Elastically Constructed Hd Map With Multi-Sensor Fusion Engine System

Lidar-Inertial Navigation Based on Map Aided Distance Constraint and Factor Graph Optimization

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