Yilong Jiang, Ting Wang, Shiliang Shao, Lebing Wang
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Next, point cloud features are tightly coupled with IMU angle constraints, ground constraints and gravity constraints for graph-based optimization in the back-end. Subsequently, the cumulative error is reduced by adding loop closure detection.\n\n\nFindings\nThe algorithm is tested using a public data set containing indoor and outdoor scenarios. The results confirm that the proposed algorithm has high accuracy and robustness.\n\n\nOriginality/value\nTo improve the accuracy and robustness of SLAM, this method proposed in the paper introduced the NDT algorithm in the front-end and designed ground constraints and gravity constraints in the back-end. The proposed method has a satisfactory performance when applied to ground-based mobile robots in complex environments experiments.\n","PeriodicalId":54987,"journal":{"name":"Industrial Robot-The International Journal of Robotics Research and Application","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"3D SLAM based on NDT matching and ground constraints for ground robots in complex environments\",\"authors\":\"Yilong Jiang, Ting Wang, Shiliang Shao, Lebing Wang\",\"doi\":\"10.1108/ir-05-2022-0128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nIn large-scale environments and unstructured scenarios, the accuracy and robustness of traditional light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) algorithms are reduced, and the algorithms might even be completely ineffective. To overcome these problems, this study aims to propose a 3D LiDAR SLAM method for ground-based mobile robots, which uses a 3D LiDAR fusion inertial measurement unit (IMU) to establish an environment map and realize real-time localization.\\n\\n\\nDesign/methodology/approach\\nFirst, we use a normal distributions transform (NDT) algorithm based on a local map with a corresponding motion prediction model for point cloud registration in the front-end. Next, point cloud features are tightly coupled with IMU angle constraints, ground constraints and gravity constraints for graph-based optimization in the back-end. Subsequently, the cumulative error is reduced by adding loop closure detection.\\n\\n\\nFindings\\nThe algorithm is tested using a public data set containing indoor and outdoor scenarios. 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3D SLAM based on NDT matching and ground constraints for ground robots in complex environments
Purpose
In large-scale environments and unstructured scenarios, the accuracy and robustness of traditional light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) algorithms are reduced, and the algorithms might even be completely ineffective. To overcome these problems, this study aims to propose a 3D LiDAR SLAM method for ground-based mobile robots, which uses a 3D LiDAR fusion inertial measurement unit (IMU) to establish an environment map and realize real-time localization.
Design/methodology/approach
First, we use a normal distributions transform (NDT) algorithm based on a local map with a corresponding motion prediction model for point cloud registration in the front-end. Next, point cloud features are tightly coupled with IMU angle constraints, ground constraints and gravity constraints for graph-based optimization in the back-end. Subsequently, the cumulative error is reduced by adding loop closure detection.
Findings
The algorithm is tested using a public data set containing indoor and outdoor scenarios. The results confirm that the proposed algorithm has high accuracy and robustness.
Originality/value
To improve the accuracy and robustness of SLAM, this method proposed in the paper introduced the NDT algorithm in the front-end and designed ground constraints and gravity constraints in the back-end. The proposed method has a satisfactory performance when applied to ground-based mobile robots in complex environments experiments.
期刊介绍:
Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world.
The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to:
Automatic assembly
Flexible manufacturing
Programming optimisation
Simulation and offline programming
Service robots
Autonomous robots
Swarm intelligence
Humanoid robots
Prosthetics and exoskeletons
Machine intelligence
Military robots
Underwater and aerial robots
Cooperative robots
Flexible grippers and tactile sensing
Robot vision
Teleoperation
Mobile robots
Search and rescue robots
Robot welding
Collision avoidance
Robotic machining
Surgical robots
Call for Papers 2020
AI for Autonomous Unmanned Systems
Agricultural Robot
Brain-Computer Interfaces for Human-Robot Interaction
Cooperative Robots
Robots for Environmental Monitoring
Rehabilitation Robots
Wearable Robotics/Exoskeletons.