LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine

Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.

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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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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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Enhanced Simultaneous Localization and Mapping (ESLAM) for Mobile Robots

International Journal of Humanoid Robotics ◽

10.1142/s0219843617500074 ◽

2017 ◽

Vol 14 (02) ◽

pp. 1750007 ◽

Cited By ~ 2

Author(s):

Chen-Chien Hsu ◽

Wei-Yen Wang ◽

Tung-Yuan Lin ◽

Yin-Tien Wang ◽

Teng-Wei Huang

Keyword(s):

Maximum Likelihood ◽

Mobile Robots ◽

Real Time ◽

Simultaneous Localization And Mapping ◽

Mapping Accuracy ◽

Satisfactory Accuracy ◽

Computation Efficiency ◽

Localization And Mapping ◽

Execution Speed

FastSLAM, such as FastSLAM 1.0 and FastSLAM 2.0, is a popular algorithm to solve the simultaneous localization and mapping (SLAM) problem for mobile robots. In real environments, however, the execution speed by FastSLAM would be too slow to achieve the objective of real-time design with a satisfactory accuracy because of excessive comparisons of the measurement with all the existing landmarks in particles, particularly when the number of landmarks is drastically increased. In this paper, an enhanced SLAM (ESLAM) is proposed, which uses not only odometer information but also sensor measurements to estimate the robot’s pose in the prediction step. Landmark information that has the maximum likelihood is then used to update the robot’s pose before updating the landmarks’ location. Compared to existing FastSLAM algorithms, the proposed ESLAM algorithm has a better performance in terms of computation efficiency as well as localization and mapping accuracy as demonstrated in the illustrated examples.

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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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SD-VIS: A Fast and Accurate Semi-Direct Monocular Visual-Inertial Simultaneous Localization and Mapping (SLAM)

Sensors ◽

10.3390/s20051511 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1511 ◽

Cited By ~ 1

Author(s):

Quanpan Liu ◽

Zhengjie Wang ◽

Huan Wang

Keyword(s):

Real Time ◽

Measurement Errors ◽

Direct Method ◽

Simultaneous Localization And Mapping ◽

Sliding Window ◽

High Accuracy ◽

Fast Tracking ◽

Time Performance ◽

Localization And Mapping ◽

Feature Based

In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.

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Real-Time Expanded Field-of-View for Minimally Invasive Surgery Using Multi-Camera Visual Simultaneous Localization and Mapping

Sensors ◽

10.3390/s21062106 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2106

Author(s):

Ahmed Afifi ◽

Chisato Takada ◽

Yuichiro Yoshimura ◽

Toshiya Nakaguchi

Keyword(s):

Minimally Invasive Surgery ◽

Minimally Invasive ◽

Real Time ◽

Invasive Surgery ◽

Simultaneous Localization And Mapping ◽

Field Of View ◽

Time Dynamic ◽

Matrix Estimation ◽

Localization And Mapping

Minimally invasive surgery is widely used because of its tremendous benefits to the patient. However, there are some challenges that surgeons face in this type of surgery, the most important of which is the narrow field of view. Therefore, we propose an approach to expand the field of view for minimally invasive surgery to enhance surgeons’ experience. It combines multiple views in real-time to produce a dynamic expanded view. The proposed approach extends the monocular Oriented features from an accelerated segment test and Rotated Binary robust independent elementary features—Simultaneous Localization And Mapping (ORB-SLAM) to work with a multi-camera setup. The ORB-SLAM’s three parallel threads, namely tracking, mapping and loop closing, are performed for each camera and new threads are added to calculate the relative cameras’ pose and to construct the expanded view. A new algorithm for estimating the optimal inter-camera correspondence matrix from a set of corresponding 3D map points is presented. This optimal transformation is then used to produce the final view. The proposed approach was evaluated using both human models and in vivo data. The evaluation results of the proposed correspondence matrix estimation algorithm prove its ability to reduce the error and to produce an accurate transformation. The results also show that when other approaches fail, the proposed approach can produce an expanded view. In this work, a real-time dynamic field-of-view expansion approach that can work in all situations regardless of images’ overlap is proposed. It outperforms the previous approaches and can also work at 21 fps.

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Pedestrian Navigation System with Trinal-IMUs for Drastic Motions

Sensors ◽

10.3390/s20195570 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5570

Author(s):

Yiming Ding ◽

Zhi Xiong ◽

Wanling Li ◽

Zhiguo Cao ◽

Zhengchun Wang

Keyword(s):

Navigation System ◽

Inertial Sensor ◽

Estimation Error ◽

Simultaneous Localization And Mapping ◽

Step Length ◽

Mapping Method ◽

Zero Crossing ◽

Pedestrian Navigation ◽

Length Estimation ◽

Localization And Mapping

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m2.

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