DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 황보제민 | - |
dc.contributor.author | Lee, Jeong Hyun | - |
dc.contributor.author | 이정현 | - |
dc.date.accessioned | 2024-07-30T19:30:36Z | - |
dc.date.available | 2024-07-30T19:30:36Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096052&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321347 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2024.2,[iii, 25 p. :] | - |
dc.description.abstract | In the realm of autonomous mobile robots, safe navigation through unpaved outdoor environments remains a challenging task. Due to the high-dimensional nature of sensor data, extracting relevant information becomes a complex problem, which hinders adequate perception and path planning. Previous works have shown promising performances in extracting global features from full-sized images. However, they often face challenges in capturing essential local information. In this paper, we propose Crop-LSTM, which iteratively takes cropped image patches around the current robot’s position and predicts the future position, orientation, and bumpiness. Our method performs local feature extraction by paying attention to corresponding image patches along the predicted robot trajectory in the 2D image plane. This enables more accurate predictions of the robot’s future trajectory. With our wheeled mobile robot platform Raicart, we demonstrated the effectiveness of Crop-LSTM for point-goal navigation in an unpaved outdoor environment. Our method enabled safe and robust navigation using RGBD images in challenging unpaved outdoor terrains. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 차량 자율주행▼a딥러닝 방법▼a차량 동역학 모델 | - |
dc.subject | Autonomous vehicle navigation▼aDeep learning methods▼aVehicle dynamics | - |
dc.title | Learning vehicle dynamics from cropped image patches for robot navigation in unpaved outdoor terrains | - |
dc.title.alternative | 비포장 험지 자율주행을 위한 잘린 이미지를 사용한 차량 동역학 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :김재철AI대학원, | - |
dc.contributor.alternativeauthor | Hwangbo, Jemin | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.