Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection

Reducing the cumulative error in the process of simultaneous localization and mapping (SLAM) has always been a hot issue. In this paper, in order to improve the localization and mapping accuracy of ground vehicles, we proposed a novel optimized lidar odometry and mapping method using ground plane constraints and SegMatch-based loop detection. We only used the lidar point cloud to estimate the pose between consecutive frames, without any other sensors, such as Global Positioning System (GPS) and Inertial Measurement Unit (IMU). Firstly, the ground plane constraints were used to reduce matching errors. Then, based on more accurate lidar odometry obtained from lidar odometry and mapping (LOAM), SegMatch completed segmentation matching and loop detection to optimize the global pose. The neighborhood search was also used to accomplish the loop detection task in case of failure. Finally, the proposed method was evaluated and compared with the existing 3D lidar SLAM methods. Experiment results showed that the proposed method could realize low drift localization and dense 3D point cloud map construction.

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3D LiDAR Point Cloud Loop Detection Based on Dynamic Object Removal

10.1109/rcar52367.2021.9517428 ◽

2021 ◽

Author(s):

Panyun Ding ◽

Zhihui Wang

Keyword(s):

Point Cloud ◽

Dynamic Object ◽

Object Removal ◽

Loop Detection ◽

3D Lidar

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Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

Applied Sciences ◽

10.3390/app9163264 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3264 ◽

Cited By ~ 2

Author(s):

Xujie Kang ◽

Jing Li ◽

Xiangtao Fan ◽

Wenhui Wan

Keyword(s):

Real Time ◽

Point Cloud ◽

Closed Loop ◽

Simultaneous Localization And Mapping ◽

Motion Blur ◽

Long Distance ◽

Terrestrial Lidar ◽

Map Construction ◽

Localization And Mapping ◽

Camera Pose

In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

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Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment

Sensors ◽

10.3390/s19132915 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2915 ◽

Cited By ~ 11

Author(s):

Zhuli Ren ◽

Liguan Wang ◽

Lin Bi

Keyword(s):

Point Cloud ◽

Underground Mining ◽

Mine Safety ◽

Measurement Unit ◽

3D Point Cloud ◽

Point Cloud Registration ◽

Mining Environment ◽

Localization And Mapping ◽

Key Frames ◽

Low Efficiency

Unmanned mining is one of the most effective methods to solve mine safety and low efficiency. However, it is the key to accurate localization and mapping for underground mining environment. A novel graph simultaneous localization and mapping (SLAM) optimization method is proposed, which is based on Generalized Iterative Closest Point (GICP) three-dimensional (3D) point cloud registration between consecutive frames, between consecutive key frames and between loop frames, and is constrained by roadway plane and loop. GICP-based 3D point cloud registration between consecutive frames and consecutive key frames is first combined to optimize laser odometer constraints without other sensors such as inertial measurement unit (IMU). According to the characteristics of the roadway, the innovative extraction of the roadway plane as the node constraint of pose graph SLAM, in addition to automatic removing the noise point cloud to further improve the consistency of the underground roadway map. A lightweight and efficient loop detection and optimization based on rules and GICP is designed. Finally, the proposed method was evaluated in four scenes (such as the underground mine laboratory), and compared with the existing 3D laser SLAM method (such as Lidar Odometry and Mapping (LOAM)). The results show that the algorithm could realize low drift localization and point cloud map construction. This method provides technical support for localization and navigation of underground mining environment.

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An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

Sensors ◽

10.3390/s21227428 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7428

Author(s):

Wennan Chai ◽

Chao Li ◽

Mingyue Zhang ◽

Zhen Sun ◽

Hao Yuan ◽

...

Keyword(s):

Measurement Errors ◽

Inertial Navigation ◽

Dead Reckoning ◽

Optimization Method ◽

Measurement Unit ◽

Vanishing Point ◽

Indoor Environments ◽

Localization And Mapping ◽

Loop Detection ◽

Pedestrian Dead Reckoning

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

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The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Sensors ◽

10.3390/s20185421 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5421

Author(s):

Yang Li ◽

Yutong Liu ◽

Yanping Wang ◽

Yun Lin ◽

Wenjie Shen

Keyword(s):

Millimeter Wave ◽

Point Cloud ◽

Radar Data ◽

Measurement Unit ◽

Small Data ◽

Millimeter Wave Radar ◽

Localization And Mapping ◽

Wave Radar ◽

Data Volume ◽

The Face

Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into “Multi-scan,” the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a “Scan to Map” point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

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Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards

Sensors ◽

10.3390/s21020339 ◽

2021 ◽

Vol 21 (2) ◽

pp. 339

Author(s):

Kai Wang ◽

Jun Zhou ◽

Wenhai Zhang ◽

Baohua Zhang

Keyword(s):

Point Cloud ◽

Search Algorithm ◽

Clustering Algorithms ◽

Fruit Trees ◽

Measurement Unit ◽

Scanning System ◽

Localization And Mapping ◽

Intelligent Measurement ◽

Canopy Volume ◽

Slam Algorithm

To meet the demand for canopy morphological parameter measurements in orchards, a mobile scanning system is designed based on the 3D Simultaneous Localization and Mapping (SLAM) algorithm. The system uses a lightweight LiDAR-Inertial Measurement Unit (LiDAR-IMU) state estimator and a rotation-constrained optimization algorithm to reconstruct a point cloud map of the orchard. Then, Statistical Outlier Removal (SOR) filtering and European clustering algorithms are used to segment the orchard point cloud from which the ground information has been separated, and the k-nearest neighbour (KNN) search algorithm is used to restore the filtered point cloud. Finally, the height of the fruit trees and the volume of the canopy are obtained by the point cloud statistical method and the 3D alpha-shape algorithm. To verify the algorithm, tracked robots equipped with LIDAR and an IMU are used in a standardized orchard. Experiments show that the system in this paper can reconstruct the orchard point cloud environment with high accuracy and can obtain the point cloud information of all fruit trees in the orchard environment. The accuracy of point cloud-based segmentation of fruit trees in the orchard is 95.4%. The R2 and Root Mean Square Error (RMSE) values of crown height are 0.93682 and 0.04337, respectively, and the corresponding values of canopy volume are 0.8406 and 1.5738, respectively. In summary, this system achieves a good evaluation result of orchard crown information and has important application value in the intelligent measurement of fruit trees.

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Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2020.21 ◽

2020 ◽

Vol 9 ◽

Author(s):

Jian Wu ◽

Qingxiong Yang

Keyword(s):

Point Cloud ◽

Large Scale ◽

Ground Plane ◽

Semantic Segmentation ◽

Point Clouds ◽

Autonomous Driving ◽

Urban Environments ◽

Cloud Data ◽

Dense Point ◽

3D Lidar

In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed. In practice, the resolution of a LiDAR sensor installed in a self-driving vehicle is relatively low and thus the acquired point cloud is indeed quite sparse. While recent work on dense point cloud segmentation has achieved promising results, the performance is relatively low when directly applied to sparse point clouds. This paper is focusing on semantic segmentation of the sparse point clouds obtained from 32-channel LiDAR sensor with deep neural networks. The main contribution is the integration of the ground information which is used to group ground points far away from each other. Qualitative and quantitative experiments on two large-scale point cloud datasets show that the proposed method outperforms the current state-of-the-art.

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TOWARDS GLOBALLY CONSISTENT SCAN MATCHINGWITH GROUND TRUTH INTEGRATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w2-59-2015 ◽

2015 ◽

Vol XL-3/W2 ◽

pp. 59-64

Author(s):

J. Gailis ◽

A. Nüchter

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Simultaneous Localization And Mapping ◽

Ground Truth ◽

Mapping Method ◽

Coordinate Frame ◽

Scan Matching ◽

Localization And Mapping ◽

Real World Datasets ◽

Ground Truth Information

The scan matching based simultaneous localization and mapping method with six dimensional poses is capable of creating a three dimensional point cloud map of the environment, as well as estimating the six dimensional path that the vehicle has travelled. The essence of it is the registering and matching of sequentially acquired 3D laser scans, while moving along a path, in a common coordinate frame in order to provide 6D pose estimations at the respective positions, as well as create a three dimensional map of the environment. An approach that could drastically improve the reliability of acquired data is to integrate available ground truth information. This paper is about implementing such functionality as a contribution to 6D SLAM (simultaneous localization and mapping with 6 DoF) in the 3DTK – The 3D Toolkit software (Nüchter and Lingemann, 2011), as well as test the functionality of the implementation using real world datasets.

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Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM

Sensors ◽

10.3390/s19010023 ◽

2018 ◽

Vol 19 (1) ◽

pp. 23 ◽

Cited By ~ 4

Author(s):

Michiel Vlaminck ◽

Hiep Luong ◽

Wilfried Philips

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Correction Method ◽

Correction Algorithm ◽

Loop Correction ◽

Registration Method ◽

Trade Offs ◽

Loop Detection ◽

3D Lidar ◽

Correction System

In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques. In addition, we accelerated the computation of the global point cloud matcher by a factor of 2–4, exploiting the GPU to its maximum. Experiments demonstrated that our combined approach more reliably detects loops in lidar data compared to other point cloud matchers as it leads to better precision–recall trade-offs: for nearly 100% recall, we gain up to 7% in precision. Finally, we present a novel loop correction algorithm that leads to an improvement by a factor of 2 on the average and median pose error, while at the same time only requires a handful of seconds to complete.

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Research on NDT-based Positioning for Autonomous Driving

E3S Web of Conferences ◽

10.1051/e3sconf/202125702055 ◽

2021 ◽

Vol 257 ◽

pp. 02055

Author(s):

Sijia Liu ◽

Jie Luo ◽

Jinmin Hu ◽

Haoru Luo ◽

Yu Liang

Keyword(s):

Real Time ◽

Electromagnetic Interference ◽

Autonomous Vehicles ◽

Autonomous Navigation ◽

Point Cloud ◽

Autonomous Driving ◽

Mapping Method ◽

Signal Loss ◽

Positioning Accuracy ◽

Localization And Mapping

Autonomous driving technology is one of the currently popular technologies, while positioning is the basic problem of autonomous navigation of autonomous vehicles. GPS is widely used as a relatively mature solution in the outdoor open road environment. However, GPS signals will be greatly affected in a complex environment with obstruction and electromagnetic interference, even signal loss may occur if serious, which has a great impact on the accuracy, stability and reliability of positioning. For the time being, L4 and most L3 autonomous driving modules still provide registration and positioning based on the high-precision map constructed. Based on this, this paper elaborates on the reconstruction of the experimental scene environment, using the SLAM (simultaneous localization and mapping) method to construct a highprecision point cloud map. On the constructed prior map, the 3D laser point cloud NDT matching method is used for real-time positioning, which is tested and verified on the “JAC Electric Vehicle” platform. The experimental results show that this algorithm has high positioning accuracy and its real-time performance meets the requirements, which can replace GPS signals to complete the positioning of autonomous vehicles when there is no GPS signal or the GPS signal is weak, and provide positioning accuracy meeting the requirements.

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