3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes

Robotics and Autonomous Systems

Volumn 83, Issue , 2016, Pages 299-311

  Asvadi, Alireza ^a   Premebida, Cristiano ^a   Peixoto, Paulo ^a   Nunes, Urbano ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

3D representation; LIDAR perception; Obstacle detection; Scene understanding

Indexed keywords

AIR NAVIGATION; AUTOMOBILE DRIVERS; COMPUTER VISION; INERTIAL NAVIGATION SYSTEMS; INTELLIGENT SYSTEMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; NAVIGATION SYSTEMS; OPTICAL RADAR; PEDESTRIAN SAFETY; SENSORY PERCEPTION; VEHICLES;

3D REPRESENTATIONS; ARTIFICIAL PERCEPTION; DRIVING ENVIRONMENT; MOVING OBSTACLE DETECTIONS; OBSTACLE DETECTION; QUALITATIVE ASSESSMENTS; SCENE UNDERSTANDING; TIME-DEPENDENT DATA;

OBSTACLE DETECTORS;

EID: 84979710953     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2016.06.007     Document Type: Article

References (43)

1. Murphy, R., Introduction to AI Robotics. 2000, MIT press.
2. Laugier, C., Paromtchik, I., Perrollaz, M., Yong, M., Yoder, J., Tay, C., Mekhnacha, K., Negre, A., Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety. IEEE Intell. Transp. Syst. Mag. 3:4 (2011), 4–19.
3. Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N., Keller, C., Kaus, E., Herrtwich, R., Rabe, C., Pfeiffer, D., Lindner, F., Stein, F., Erbs, F., Enzweiler, M., Knoppel, C., Hipp, J., Haueis, M., Trepte, M., Brenk, C., Tamke, A., Ghanaat, M., Braun, M., Joos, A., Fritz, H., Mock, H., Hein, M., Zeeb, E., Making bertha drive - an autonomous journey on a historic route. IEEE Intell. Transp. Syst. Mag. 6:2 (2014), 8–20.
4. Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M.N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., Gittleman, M., Harbaugh, S., Hebert, M., Howard, T.M., Kolski, S., Kelly, A., Likhachev, M., McNaughton, M., Miller, N., Peterson, K., Pilnick, B., Rajkumar, R., Rybski, P., Salesky, B., Seo, Y.-W., Singh, S., Snider, J., Stentz, A., Whittaker, W.R., Wolkowicki, Z., Ziglar, J., Bae, H., Brown, T., Demitrish, D., Litkouhi, B., Nickolaou, J., Sadekar, V., Zhang, W., Struble, J., Taylor, M., Darms, M., Ferguson, D., Autonomous driving in urban environments: Boss and the urban challenge. J. Field Robot. 25:8 (2008), 425–466.
5. Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., Johnston, D., Klumpp, S., Langer, D., Levandowski, A., Levinson, J., Marcil, J., Orenstein, D., Paefgen, J., Penny, I., Petrovskaya, A., Pflueger, M., Stanek, G., Stavens, D., Vogt, A., Thrun, S., Junior: The stanford entry in the urban challenge. J. Field Robot. 25:9 (2008), 569–597.
6. Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H., 6d slam with approximate data association. 12th International Conference on Advanced Robotics, 2005. ICAR’05. Proceedings, 2005, IEEE, 242–249.
7. D. Sack, W. Burgard, A comparison of methods for line extraction from range data, in: Proc. of the 5th IFAC Symposium on Intelligent Autonomous Vehicles, IAV, 2004.
8. M. Oliveira, V. Santos, A. Sappa, P. Dias, Scene representations for autonomous driving: an approach based on polygonal primitives, in: 2nd Iberian Robotics Conference, 2015.
9. Pascoal, R., Santos, V., Premebida, C., Nunes, U., Simultaneous segmentation and superquadrics fitting in laser-range data. IEEE Trans. Veh. Technol. 64:2 (2015), 441–452.
10. Moravec, H.P., Elfes, A., High resolution maps from wide angle sonar. 1985 IEEE International Conference on Robotics and Automation. Proceedings, Vol. 2, 1985, IEEE, 116–121.
11. Herbert, M., Caillas, C., Krotkov, E., Kweon, I.S., Kanade, T., Terrain mapping for a roving planetary explorer. 1989 IEEE International Conference on Robotics and Automation, 1989. Proceedings, 1989, IEEE, 997–1002.
12. Pfaff, P., Triebel, R., Burgard, W., An efficient extension to elevation maps for outdoor terrain mapping and loop closing. Int. J. Robot. Res. 26:2 (2007), 217–230.
13. Triebel, R., Pfaff, P., Burgard, W., Multi-level surface maps for outdoor terrain mapping and loop closing. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, IEEE, 2276–2282.
14. Roth-Tabak, Y., Jain, R., Building an environment model using depth information. Computer 22:6 (1989), 85–90.
15. H. Moravec, Robot spatial perceptionby stereoscopic vision and 3D evidence grids, Perception, (September).
16. Haehnel, D., Mapping with mobile robots. (Ph.D. thesis), 2004, University of Freiburg, Department of Computer Science.
17. Ryde, J., Hu, H., 3D mapping with multi-resolution occupied voxel lists. Auton. Robots 28:2 (2010), 169–185.
18. Douillard, B., Underwood, J., Melkumyan, N., Singh, S., Vasudevan, S., Brunner, C., Quadros, A., Hybrid elevation maps: 3D surface models for segmentation. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, IEEE, 1532–1538.
19. Meagher, D., Geometric modeling using octree encoding. Comput. Graph. Image Process. 19:2 (1982), 129–147.
20. Hornung, A., Wurm, K., Bennewitz, M., Stachniss, C., Burgard, W., Octomap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34:3 (2013), 189–206.
21. Dryanovski, I., Morris, W., Xiao, J., Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, IEEE, 1553–1559.
22. Discant, A., Rogozan, A., Rusu, C., Bensrhair, A., Sensors for obstacle detection-a survey. 30th International Spring Seminar on Electronics Technology, 2007, IEEE, 100–105.
23. Bernini, N., Bertozzi, M., Castangia, L., Patander, M., Sabbatelli, M., Real-time obstacle detection using stereo vision for autonomous ground vehicles: A survey. 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), 2014, IEEE, 873–878.
24. Zhang, Z., Weiss, R., Hanson, A.R., Obstacle detection based on qualitative and quantitative 3D reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 19:1 (1997), 15–26.
25. Petrovskaya, A., Perrollaz, M., Oliveira, L., Spinello, L., Triebel, R., Makris, A., Yoder, J.-D., Laugier, C., Nunes, U., Bessiere, P., Awareness of road scene participants for autonomous driving. Handbook of Intelligent Vehicles, 2012, Springer, 1383–1432.
26. Sivaraman, S., Trivedi, M.M., Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14:4 (2013), 1773–1795.
27. Vatavu, A., Danescu, R., Nedevschi, S., Stereovision-based multiple object tracking in traffic scenarios using free-form obstacle delimiters and particle filters. IEEE Trans. Intell. Transp. Syst. 16:1 (2015), 498–511.
28. A. Asvadi, P. Peixoto, U. Nunes, Detection and tracking of moving objects using 2.5d motion grids, in: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, ITSC, 2015, pp. 788–793.
29. D. Pfeiffer, U. Franke, Efficient representation of traffic scenes by means of dynamic stixels, in: Intelligent Vehicles Symposium, IV, 2010 IEEE, 2010, pp. 217–224.
30. A. Broggi, S. Cattani, M. Patander, M. Sabbatelli, P. Zani, A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision, in: 2013 16th International IEEE Conference on Intelligent Transportation Systems - ITSC, 2013, pp. 71–76.
31. A. Azim, O. Aycard, Layer-based supervised classification of moving objects in outdoor dynamic environment using 3D laser scanner, in: Intelligent Vehicles Symposium Proceedings, 2014 IEEE, 2014, pp. 1408–1414.
32. Labayrade, R., Aubert, D., Tarel, J.-P., Real time obstacle detection in stereovision on non flat road geometry through” v-disparity” representation. Intelligent Vehicle Symposium, 2002. IEEE, Vol. 2, 2002, IEEE, 646–651.
33. Sappa, A.D., Herrero, R., Dornaika, F., Gerónimo, D., López, A., Road approximation in euclidean and v-disparity space: a comparative study. Computer Aided Systems Theory–EUROCAST 2007, 2007, Springer, 1105–1112.
34. Oniga, F., Nedevschi, S., Processing dense stereo data using elevation maps: Road surface, traffic isle, and obstacle detection. IEEE Trans. Veh. Technol. 59:3 (2010), 1172–1182.
35. Fischler, M.A., Bolles, R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24:6 (1981), 381–395.
36. Petrovskaya, A.V., Towards Dependable Robotic Perception. 2011, Stanford University.
37. Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., et al. A perception-driven autonomous urban vehicle. J. Field Robot. 25:10 (2008), 727–774.
38. Ess, A., Schindler, K., Leibe, B., Van Gool, L., Object detection and tracking for autonomous navigation in dynamic environments. Int. J. Robot. Res. 29:14 (2010), 1707–1725.
39. Badino, H., Franke, U., Pfeiffer, D., The stixel world-a compact medium level representation of the 3D-world. Pattern Recognition, 2009, Springer, 51–60.
40. Franke, U., Rabe, C., Badino, H., Gehrig, S., 6d-vision: Fusion of stereo and motion for robust environment perception. Pattern Recognition, 2005, Springer, 216–223.
41. Asvadi, A., Peixoto, P., Nunes, U., Two-stage static/dynamic environment modeling using voxel representation. Robot 2015: Second Iberian Robotics Conference, 2016, Springer, 465–476.
42. Besl, P.J., McKay, N.D., Method for registration of 3-d shapes. Robotics-DL Tentative, 1992, International Society for Optics and Photonics, 586–606.
43. Geiger, A., Lenz, P., Stiller, C., Urtasun, R., Vision meets robotics: The KITTI dataset. Int. J. Robot. Res. 32:11 (2013), 1231–1237.