본문 바로가기
KERD
논문KCI 등재

3차원 레이저 스캐너를 활용한 야지에서의 주행가능 영역 검출 방법

Traversable Region Detection Method in rough terrain using 3D Laser Scanner

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류컴퓨터학

초록

본 논문에서는 무인 지상 차량(UGV)이 야지 조건에서 주행할 수 있는 영역을 탐지하는 방법을 제안한다. UGV는 일반 차량이 주행하는 포장도로가 아닌 산이나 들판과 같은 야지에서 주행이 가능한 지역을 판단하는 것이 중요하다. 이를 위해 우선 3D 레이저 스캐너를 사용하여 주행 환경을 거리 이미지 형태로 변환한다. 이를 활용하여 한 프레임에 수백만 개가 계측되는 레이저 스캐너의 데이터를 2차원 이미지로 빠르게 처리한다. 거리 이미지를 사용하여 주행 차량의 진행 방향뿐만 아니라 좌우 경사의 기울기를 고려하여 주행이 가능한 영역을 탐지한다. 또한 거리 이미지에서 인접한 픽셀 사이의 각도 차이를 이용하여 각도 이미지를 만들고 이에 스무딩 방법을 적용한다. 이를 통해 차량이 존재하는 위치에서부터 탐색을 시작하여 현재 위치와 유사한 노면의 기울기를 가진 영역을 검사한다. 이를 통해 최종적으로 야지의 조건에서 UGV가 주행이 가능한 영역을 판단하는 알고리즘을 제안한다. 그리고 제안한 방법의 우수성을 검증하기 위해 실제로 야지를 주행하여 실제 데이터를 수집하고 이를 기반으로 제안한 방법과 기존 방법과의 비교를 진행한다.

Abstract

In this paper, I propose a traversable region detection method by which an unmanned ground vehicle (UGV) can drive in an off-road condition. It is necessary for UGVs to determine traversable regions when travelling in off-road conditions such as mountains or fields, as opposed to driving on a paved road. To this end, a 3D laser scanner is used to measure the surrounding environment accurately. To process a number of point clouds from a 3D laser scanner efficiently, I propose a range image based traversable region detection approach. In addition, by proposing a traversable detection algorithm that considers not only the traveling direction of the vehicle but also the slope of the left and right slopes, it limits robust performance in various rough terrain conditions. In order to verify the superiority of this method, actual data is collected by driving on rough terrain, and based on this, the proposed method and the existing method are compared.

참고문헌 (44)

  1. F. Homm, "Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers", Proceedings of IEEE Intelligent Vehicles Symposium, pp.969~974 (2011)
  2. H. Guan, "Using mobile laser scanning data for automated extraction of road markings", ISPRS J. Photogramm. Remote Sens, 87, pp.93~107 (2014)
  3. P. Kumar, "Automated road markings extraction from mobile laser scanning data", Int. J. Appl. Earth Obs. Geoinf, 32, pp.125~137 (2014)
  4. A. Hata, "Road Marking Detection Using LIDAR Reflective Intensity Data and its Application to Vehicle Localization", in Proceedings of IEEE Conference on Intelligent Transportation Systems (2014)
  5. H. Guan, "Automated Road Information Extraction From Mobile Laser Scanning Data", IEEE Trans. Intell. Transp. Syst, 16(1), pp.194~205 (2015)
  6. Y. Zhang, "3D LIDAR-Based Intersection Recognition and Road Boundary Detection Method for Unmanned Ground Vehicle", Proceedings of IEEE Conference on Intelligent Transportation Systems, 2015, pp.499~504 (2015)
  7. J. Han, "Enhanced Road Boundary and Obstacle Detection Using a Downward-Looking LIDAR Sensor", IEEE Trans. Veh. Technol, 61(3), pp.971~985 (2012)
  8. F. Moosmann, "Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion", Proceedings of IEEE Intelligent Vehicles Symposium, pp.215~220 (2009)
  9. M. Himmelsbach, "Fast segmentation of 3D point clouds for ground vehicles", Proceedings of IEEE Intelligent Vehicles Symposium, pp.560~565 (2010)
  10. Y. Choe, "Fast point cloud segmentation for an intelligent vehicle using sweeping 2D laser scanners", Proc. Int. Conf. Ubiquitous Robot. Ambient Intell, pp.38~43 (2012)
  11. T. Chen, "Gaussian-Process-Based Real-Time Ground Segmentation for Autonomous Land Vehicles", J. Intell. Robot. Syst, 76(3), pp.563~582 (2014)
  12. I. Bogoslavskyi, "Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation", Proc. IEEE, 2016, ~
  13. I. Bogoslavskyi, "Efficient Online Segmentation for Sparse 3D Laser Scans", J. Photogramm. Remote Sens. Geoinf. Sci, 85(1), pp.41~52 (2017)
  14. L. Caltagirone, "Fast LIDAR-based road detection using fully convolutional neural networks", Proceedings of IEEE Intelligent Vehicles Symposium, pp.1019~1024
  15. P. Babahajiani, "Urban 3D segmentation and modelling from street view images and LiDAR point clouds", Mach. Vis. Appl, 28(7), pp.679~694 (2017)
  16. D. Zermas, "Fast segmentation of 3D point clouds : A paradigm on LiDAR data for autonomous vehicle applications", Proceedings of IEEE International Conference on Robotics and Automation, pp.5067~5073 (2017)
  17. M. -O. Shin, "Real-Time and Accurate Segmentation of 3-D Point Clouds Based on Gaussian Process Regression", IEEE Trans. Intell. Transp. Syst, 18(12), pp.1~15 (2017)
  18. T. Weiss, "Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids", Proceedings of IEEE Intelligent Vehicles Symposium, pp.184~189 (2007)
  19. F. Homm, "Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection", Proceedings of IEEE Intelligent Vehicles Symposium, pp.1006~1013 (2010)
  20. J. An, "Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner", Sensors (Switzerland), 16(7) (2016)
  21. R. Jungnickel, "Efficient Automotive Grid Maps using a Sensor Ray based Refinement Process", Proceedings of IEEE Intelligent Vehicles Symposium (2016)
  22. H. M. Eraqi, "Static Free Space Detection with Laser Scanner using Occupancy Grid Maps", Proceedings of IEEE Conference on Intelligent Transportation Systems (2018)
  23. C. -C. Wang, "Online simultaneous localization and mapping with detection and tracking of moving objects : Theory and results from a ground vehicle in crowded urban areas", Proceedings of IEEE International Conference on Robotics and Automation, 1, pp.842~849 (2003)
  24. C. -C. Wang, "Simultaneous Localization, Mapping and Moving Object Tracking Moving Object Tracking", Int. J. Rob. Res, 26(9), pp.889~916 (2007)
  25. T.-D. Vu, "Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments", Proceedings of IEEE Intelligent Vehicles Symposium, pp.190~195 (2007)
  26. T. D. Vu, "Grid-based localization and local mapping with moving object detection and tracking", Inf. Fusion, 12(1), pp.58~69 (2011)
  27. S. Steyer, "Object tracking based on evidential dynamic occupancy grids in urban environments", Proceedings of IEEE Intelligent Vehicles Symposium, pp.1064~1070 (2017)
  28. S. Hoermann, "Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling", arXiv, ~ (2017)
  29. F. Gies, "Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps", Proceedings of IEEE Conference on Intelligent Transportation Systems, pp.3859~3865 (2018)
  30. S. Hoermann, "Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation", arXiv, ~ (2018)
  31. P. Papadakis, "Terrain traversability analysis methods for unmanned ground vehicles : A survey", Engineering Applications of Artificial Intelligence, 26(4), pp.1373~1385 (2013), Elsevier Ltd
  32. K. Yang, "Unifying terrain awareness for the visually impaired through real-time semantic segmentation", Sensors (Switzerland), 18(5), pp.1506~ (2018)
  33. P. Jiang, "RELLIS-3D Dataset : Data, Benchmarks and Analysis", arXiv, ~ (2020)
  34. K. Viswanath, "OFFSEG : A Semantic Segmentation Framework For Off-Road Driving", arXiv, ~ (2021)
  35. J. Sock, "Probabilistic traversability map generation using 3D-LIDAR and camera", Proceedings-IEEE International Conference on Robotics and Automation, 2016, pp.5631~5637 (2016)
  36. J. Ahtiainen, "Normal Distributions Transform Traversability Maps : LIDAR-Only Approach for Traversability Mapping in Outdoor Environments", J. Field. Robot, 34(3), pp.600~621 (2017)
  37. J. L. Martínez, "Reactive navigation on natural environments by continuous classification of ground traversability", Sensors (Switzerland), 20(22), pp.1~18 (2020), Multidisciplinary Digital Publishing Institute
  38. 이희성, "Probabilistic Background Subtraction in a Video-based Recognition System", KSII Transactions on Internet and Information Systems, 5(4), pp.782~804 (2011), 한국인터넷정보학회
  39. K. Kim, "Probabilistic ship detection and classification using deep learning", Appl. Sci., 8(6), pp.936~ (2018)
  40. S. Thrun, "Stanley : The Robot that Won the DARPA Grand Challenge", J. Field. Robot, 23(9), pp.661~692 (2006)
  41. B. Douillard, "On the segmentation of 3D lidar point clouds", Proceedings of IEEE International Conference on Robotics and Automation, pp.2798~2805 (2011)
  42. K. Lai, "Object recognition in 3D point clouds using web data and domain adaptation", Int. J. Rob. Res, 29(8), pp.1019~1037 (2010)
  43. A. Hoover, "An Experimental Comparison of Range Image Segmentation Algorithms", IEEE Trans. Pattern Anal. Mach. Intell, 18(7), pp.673~689 (1996)
  44. A. Savitzky, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures", Anal. Chem, 36(8), pp.1627~1639 (1964)

관련 논문AI추천

이 간행물의 다른 논문 (13건)