Autonomous Navigation of Electric Wheelchairs in Urban Areas on the Basis of Self-Generated 2D Drivable Maps

Niijima, Shun; Sasaki, Yoko; Mizoguchi, Hiroshi

doi:10.1109/aim.2018.8452435

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Author details 1 Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan. 2 Department of Mechanical Engineering, Graduate School of Engineering, Tokyo Institute of…”

Section: Authors' Contributionsmentioning

confidence: 99%

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Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance against Approaching Pedestrians

Omori,

Yoshitake,

Shino

2024

Preprint

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There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both efficiency and comfort is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient and comfortable autonomous navigation method considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method can achieve efficient and comfortable autonomous navigation, especially in crowded environments.

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Section: Authors' Contributionsmentioning

confidence: 99%

“…However, accidents resulting from user errors and misjudgments in operation are reported [1]. Accordingly, developing autonomous functions independent of users' abilities is crucial, and ongoing efforts are made in this direction [2,3].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance against Approaching Pedestrians

Omori,

Yoshitake,

Shino

2024

Preprint

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“…The collected data contains only the xyz coordinates; subsequently, floor estimation is required for extracting the floor from background 3D data. Therefore, we estimated the floor based on a two-dimensional drivable map [23]. Not only floor data, but also LiDAR location are required for setting the human model deployment area.…”

Section: B Background 3d Datamentioning

confidence: 99%

Automatic Labeled LiDAR Data Generation and Distance-Based Ensemble Learning for Human Segmentation

et al. 2019

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Following the improvements in deep neural networks, state-of-the-art networks have been proposed for human segmentation using point clouds captured by light detection and ranging. However, the performance of these networks depends significantly on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain ground-truth labels; however, labeling involves high costs. Therefore, we propose an automatically labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and the background of Miraikan to generate realistic artificial data. We present 1M data generated by the proposed pipeline. Furthermore, we propose an ensemble learning based on generated data for utilizing our data generation pipeline. This paper proposes the specifications of the pipeline, data details, and explanation of ensemble learning with evaluations of various approaches.

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“…Various mobile robots use a city 3D map as prior knowledge to perform autonomous navigation. In particular, 3D maps are useful for self-pose localization [1] [2] and path planning in mobile robot navigation [3] [4]. With accurate global 3D maps of urban areas it is possible for mobile robots to navigate autonomously in a wide city area.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

et al. 2022

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Autonomous Navigation of Electric Wheelchairs in Urban Areas on the Basis of Self-Generated 2D Drivable Maps

Cited by 8 publications

References 11 publications

Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance against Approaching Pedestrians

Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance against Approaching Pedestrians

Automatic Labeled LiDAR Data Generation and Distance-Based Ensemble Learning for Human Segmentation

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

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