Robust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion

Wu, Yuanqing; Li, Yanzhou; Li, Wenhao; Li, Hongyi; Lu, Renquan

doi:10.1109/tnnls.2020.3027983

Cited by 27 publications

(7 citation statements)

References 36 publications

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“…The motion state data of UGV can usually be obtained directly or indirectly by various sensors. Different kinds of data usually correspond to different sensors [3] . For example, inertial measurement unit (IMU) can measure three-axis angle (or angular rate) and acceleration, odometer is used to record the distance have moved, etc.…”

Section: Introductionmentioning

confidence: 99%

Visualization of UGV motion state fused geospatial information

Zhang¹,

Xu²,

Liu³

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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Aimed at visualization requirements of the motion state of unmanned ground vehicles (UGV) in geospatial environment, we designed a Cesium-based geospatial information framework. Firstly, the ROS-based motion data structure and communicating method are studied. Then the motion information of UGV in real time is display in a 3D geographic environment with basic information such as maps and terrain loaded. As a result, the fusion and expression of motion data and geospatial data are accomplished, which support the application and research of internal and external information visualization, path planning, automatic control, and other aspects of UGV.

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Section: Introductionmentioning

confidence: 99%

Visualization of UGV motion state fused geospatial information

Zhang¹,

Xu²,

Liu³

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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“…The collected point clouds must be registered into the global coordinates system as the scanner moves, a process known as point cloud registration. This is a fundamental process in a variety of industries, including robotics [1], unmanned ground vehicles (UGVs) [2], 3D reconstruction [3], and 3D medical imaging [4], among others. A popular approach for aligning two frames of point clouds is to first extract key points from two point clouds and determine the correspondence between them, then estimate the transformation function by aligning the corresponding key points [5].…”

Section: Introductionmentioning

confidence: 99%

A New Approach toward Corner Detection for Use in Point Cloud Registration

Wang

Zhang

et al. 2023

Remote Sensing

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For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points are defined as the intersection of three planes. Next, graphs are constructed for subsequent point cloud registration by treating corners as vertices and sharing planes as edges. The graph-matching algorithm is then applied to determine correspondence. Finally, point clouds are registered by aligning the corresponding corner points. The proposed method was investigated by utilizing pertinent metrics on datasets with differing overlap. The results demonstrate that the proposed method can align point clouds that have low overlap, yielding an RMSE of about 0.05 cm for datasets with 90% overlap and about 0.2 cm when there is only about 10% overlap. In this situation, the other methods failed to align point clouds. In terms of time consumption, the proposed method can process a point cloud comprising 104 points in 4 s when there is high overlap. When there is low overlap, it can also process a point cloud comprising 106 points in 10 s. The contributions of this study are the definition and extraction of corner points at the geometric level, followed by the use of these corner points to register point clouds. This approach can be directly used for low-precision applications and, in addition, for coarse registration in high-precision applications.

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“…Whereas the latter has proven to be a solved problem, the first two are still limited and have to be improved. Some of the suggested solutions involve the implementation of GPS devices accompanied by three-dimensional LiDARs and inertial measurement (IMU) [ 6 , 7 ], while a few utilize 2D LiDARs [ 8 , 9 ]. However, a wide range of implementation of these methods is quite difficult owing to the high cost, complexity, and unpredictability in specific scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot

Imad

Doukhi²,

Lee³

et al. 2022

Sensors

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Feasible local motion planning for autonomous mobile robots in dynamic environments requires predicting how the scene evolves. Conventional navigation stakes rely on a local map to represent how a dynamic scene changes over time. However, these navigation stakes depend highly on the accuracy of the environmental map and the number of obstacles. This study uses semantic segmentation-based drivable area estimation as an alternative representation to assist with local motion planning. Notably, a realistic 3D simulator based on an Unreal Engine was created to generate a synthetic dataset under different weather conditions. A transfer learning technique was used to train the encoder-decoder model to segment free space from the occupied sidewalk environment. The local planner uses a nonlinear model predictive control (NMPC) scheme that inputs the estimated drivable space, the state of the robot, and a global plan to produce safe velocity commands that minimize the tracking cost and actuator effort while avoiding collisions with dynamic and static obstacles. The proposed approach achieves zero-shot transfer from a simulation to real-world environments that have never been experienced during training. Several intensive experiments were conducted and compared with the dynamic window approach (DWA) to demonstrate the effectiveness of our system in dynamic sidewalk environments.

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Robust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion

Cited by 27 publications

References 36 publications

Visualization of UGV motion state fused geospatial information

Visualization of UGV motion state fused geospatial information

A New Approach toward Corner Detection for Use in Point Cloud Registration

Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot

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