Recognition Method Applied to Smart Dump 9 Using Multi-Beam 3D LiDAR for the Tsukuba Challenge

Takita, Yoshihiro; Ohkawa, Shinya; Date, Hisashi

doi:10.20965/jrm.2016.p0451

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…In Figure 11, the magnification of the layout map is Z m = 3.3, the magnification of the aerial photograph is Z p = 1.67, the length of one side of the tile image is L t = 124.5 [m], and the resolution of the tile image is R t = 256 [pixel]. The length U of 1 [pixel] of the layout map image is expressed by Equation (9). Note that the display at 100 [%] is 1.0.…”

Section: Scale Parameter Calculationmentioning

confidence: 99%

“…Similarly, SLAM systems require manual operation of the transport vehicle to comprehensively map the work environment and construct an environment map, a process essential for labour-saving measures. In the SLAM method, self-position is estimated by matching the surroundings using a laser range sensor with an environmental map, so if there are many disturbances, such as people or obstacles, it may not be able to match the environmental map and the location may not be specified [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

Sasaki,

Tsuda,

Matsushita

2024

Electronics

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In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments.

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Section: Scale Parameter Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

Sasaki,

Tsuda,

Matsushita

2024

Electronics

View full text Add to dashboard Cite

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Generated Trajectory of Extended Lateral Guided Sensor Steering Mechanism for Steered Autonomous Vehicles in Real World Environments

Takita¹

2017

JRM

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This paper discusses the generated trajectory of an extended lateral guided sensor steering mechanism (SSM) method for a steered autonomous vehicle moving in a real world environment. In a previous study, an extended SSM was applied to the Smart Dump 9 and AR Chair robots for following preset waypoints on a map. These studies showed only the schematic idea of the method; the precise performance of the generated trajectory was not shown. This paper compares the Smart Dump 9 robot with a newly developed AR Skipper robot; these robots participated in the Tsukuba Challenge in 2015 and 2016, respectively. Finally, experimental data from the Tsukuba Challenge 2016 demonstrates the advantages of the extended SSM and developed control system.

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Creating a 3D Cuboid Map Using Multi-Layer 3D LIDAR with a Swing Mechanism

Takita

2018

JRM

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This paper proposes a method for creating 3D occupancy grid maps using multi-layer 3D LIDAR and a swing mechanism termed Swing-LIDAR. The method using Swing-LIDAR can acquire 10 times more data at a stopping position than a method that does not use Swing-LIDAR. High-definition and accurate terrain information is obtained by a coordinate transformation of the acquired data compensated for by the measured orientation of the system. In this study, we develop a method to create 3D grid maps for autonomous robots using Swing-LIDAR. To validate the method, AR Skipper is run on the created maps that are used to obtain point cloud data without a swing mechanism, and 11 sets of each local map are combined. The experimental results exhibit the differences among the maps.

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Recognition Method Applied to Smart Dump 9 Using Multi-Beam 3D LiDAR for the Tsukuba Challenge

Cited by 4 publications

References 16 publications

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

Generated Trajectory of Extended Lateral Guided Sensor Steering Mechanism for Steered Autonomous Vehicles in Real World Environments

Creating a 3D Cuboid Map Using Multi-Layer 3D LIDAR with a Swing Mechanism

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