2D 라이다 데이터베이스 기반 장애물 분류 기법
Obstacle Classification Method Based on Single 2D LIDAR Database
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참고문헌
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초록
We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.
Abstract
We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.
참고문헌 (14)
- 장원철, "센서 네트워크 기반 자율주행 자동차 제어 시스템 설계 및 구현", 대한임베디드공학회논문지, 7(5), pp.247~253 (2012), 대한임베디드공학회
- J. Wenger, "Automotive radar-status and perspective", Proceedings of Compound Semiconductor Integrated Circuit Symposium, pp.21~24 (2005)
- A. Kirchner, "Model based detection of road boundaries with a laser scanner", Proceedings of Intelligent Vehicle Symposium, pp.93~98 (1998)
- 주건엽, "자율주행자동차 개발: A1", 한국자동차공학회 논문집, 19(4), pp.146~154 (2011), 한국자동차공학회
- D. M. Cole, "Using laser range data for 3D SLAM in outdoor environments", Proceedings of International Conference on Robotics and Automation, pp.1556~1563 (2006)
- S. Thrun, "Stanley : The robot that won the DARPA grand challenge", Journal of Field Robotics, 23(9), pp.661~692 (2006)
- R. Halterman, "Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection", Proceedings of International Society for Optical Engineering, 7692, pp.224~231 (2010)
- Z. Yanjun, "A single camera based rear obstacle detection system", Proceedings of Intelligent Vehicle Symposium, pp.485~490 (2011)
- L. Iocchi, "Building 3D maps with semantic elements integrating 2D laser, stereo vision and IMU on a mobile robot", Proceedings of the 2nd International Society for Photogrammetry and Remote Sensing International Workshop 3D-ARCH (2007)
- H. C. Moon, "Obstacle detecting system for unmanned ground vehicle using laser scanner and vision", Proceedings of International Conference on Control, Automation and System, pp.1758~1761 (2007)
- 안승욱, "무인 자동차의 2차원 레이저 거리 센서를 이용한 도시 환경에서의 빠른 주변 환경 인식 방법", 로봇학회 논문지, 7(2), pp.92~100 (2012), 한국로봇학회[DOI]
- D. Haverman, "Artificial neural nets object recognition for 3D point cloud", Proceedings of Brazilian Conference on Intelligent System, pp.101~106 (2013)
- O. Hadjiliadis, "Sequential classification in point cloud of urban scenes", Proceedings of 3DPVT (2010)
- J. Hancock, "Laser intensity-based obstacle detection", Proceedings of International Conference on Intelligent Robots and System, 3, pp.1541~1546 (1998)