LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

Shan, Tixiao; Englot, Brendan

doi:10.1109/iros.2018.8594299

Cited by 1,554 publications

(912 citation statements)

References 19 publications

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“…To calculate a set of incremental motions between consecutive frames of each LiDARs, the LeGO-LOAM algorithm [5] is used. This method makes use of geometric features in environments to estimate the ego-motion.…”

Section: Methodology a Motion Estimationmentioning

confidence: 99%

Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Jiao

Liao

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements. However, the lack of precise calibration negatively affects their potential applications in localization and perception systems. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration without any calibration target, prior environmental information, and initial values of the extrinsic parameters. Our approach starts with a hand-eye calibration for automatic initialization by aligning the estimated motions of each sensor. The resulting parameters are then refined with an appearance-based method by minimizing a cost function constructed from point-plane correspondences. Experimental results on simulated and real-world data sets demonstrate the reliability and accuracy of our calibration approach. The proposed approach can calibrate a multi-LiDAR system with the rotation and translation errors less than 0.04 [rad] and 0.1 [m] respectively for a mobile platform.

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Section: Methodology a Motion Estimationmentioning

confidence: 99%

Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Jiao

Liao

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…However, as discussed in Section I, the GPS signal is vulnerable to environment changes. Therefore, researchers have been working on environment perception and AV state estimation with the use of multiple sensors [7], [8], [17]- [19]. For example, passive sensors such as cameras can be used to capture 2D/3D visual data of the environment [20], [21].…”

Section: Related Workmentioning

confidence: 99%

Low-Cost GPS-Aided LiDAR State Estimation and Map Building

Zheng

Zhu

Xue

et al. 2019

2019 IEEE International Conference on Imaging Systems and Techniques (IST)

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Using different sensors in an autonomous vehicle (AV) can provide multiple constraints to optimize AV location estimation. In this paper, we present a low-cost GPSassisted LiDAR state estimation system for AVs. Firstly, we utilize LiDAR to obtain highly precise 3D geometry data. Next, we use an inertial measurement unit (IMU) to correct point cloud misalignment caused by incorrect place recognition. The estimated LiDAR odometry and IMU measurement are then jointly optimized. We use a lost-cost GPS instead of a realtime kinematic (RTK) module to refine the estimated LiDARinertial odometry. Our low-cost GPS and LiDAR complement each other, and can provide highly accurate vehicle location information. Moreover, a low-cost GPS is much cheaper than an RTK module, which reduces the overall AV sensor cost. Our experimental results demonstrate that our proposed GPS-aided LiDAR-inertial odometry system performs very accurately. The accuracy achieved when processing a dataset collected in an industrial zone is approximately 0.14 m.

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“…Many researchers use the loop closure method to reduce the drift during map generation. LEGO-LOAM [18] uses the trajectory to estimate the loop closure, then it calculates the root mean square error between the current LiDAR frame and the key frame, that was acquired in the past as the loop closure condition. ORB-SLAM also uses loop closures to reduce the accumulation error.…”

Section: Related Workmentioning

confidence: 99%

Real-Time, Environmentally-Robust 3D LiDAR Localization

Zhu

Xue

Zheng

et al. 2019

2019 IEEE International Conference on Imaging Systems and Techniques (IST)

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Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment using GPS and LiDAR. Then, we divide the map into several small parts as the targets for cloud registration, which can not only improve the robustness but also reduce the registration time. PointLocalization allows us to fuse different kinds of odometers, which can optimize the accuracy and frequency of localization results. We evaluate our algorithm on an unmanned ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the localization results at more than 20 Hz after fusion. The algorithm can also localize the UGV in a 180-degree field of view (FOV). Using an outdated map captured six months ago, this algorithm shows great robustness, and the test results show that it can achieve an accuracy of 10 cm. PointLocalization has been tested for a period of more than six months in a crowded factory and has operated successfully over a distance of more than 2000 km.

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LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

Cited by 1,554 publications

References 19 publications

Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Low-Cost GPS-Aided LiDAR State Estimation and Map Building

Real-Time, Environmentally-Robust 3D LiDAR Localization

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