Hostname: page-component-7dd5485656-bvgqh Total loading time: 0 Render date: 2025-10-30T16:18:11.291Z Has data issue: false hasContentIssue false
  • English
  • Français

Environment perception based on LIDAR sensors for real road applications

Published online by Cambridge University Press:  24 May 2011

F. García ,
F. Jiménez ,
J. E. Naranjo ,
J. G. Zato ,
F. Aparicio ,
J. M. Armingol  and
A. de la Escalera
F. García*
Affiliation:
Universidad Carlos III de Madrid, Laboratorio de Sistemas Inteligentes Avda, De La Universidad 30, 28911 Leganés (Madrid), Spain
F. Jiménez
Affiliation:
Universidad Politécnica de Madrid, INSIA, Carretera de Valencia, km.7, 28031 Madrid, Spain
J. E. Naranjo
Affiliation:
Universidad Politécnica de Madrid, E.U. de Informática, Carretera de Valencia, km.7, 28031 Madrid, Spain
J. G. Zato
Affiliation:
Universidad Politécnica de Madrid, E.U. de Informática, Carretera de Valencia, km.7, 28031 Madrid, Spain
F. Aparicio
Affiliation:
Universidad Politécnica de Madrid, INSIA, Carretera de Valencia, km.7, 28031 Madrid, Spain
J. M. Armingol
Affiliation:
Universidad Carlos III de Madrid, Laboratorio de Sistemas Inteligentes Avda, De La Universidad 30, 28911 Leganés (Madrid), Spain
A. de la Escalera
Affiliation:
Universidad Carlos III de Madrid, Laboratorio de Sistemas Inteligentes Avda, De La Universidad 30, 28911 Leganés (Madrid), Spain
*
*Corresponding author. E-mail: fegarcia@ing.uc3m.es
Get access Rights & Permissions [Opens in a new window]

Summary

The recent developments in applications that have been designed to increase road safety require reliable and trustworthy sensors. Keeping this in mind, the most up-to-date research in the field of automotive technologies has shown that LIDARs are a very reliable sensor family. In this paper, a new approach to road obstacle classification is proposed and tested. Two different LIDAR sensors are compared by focusing on their main characteristics with respect to road applications. The viability of these sensors in real applications has been tested, where the results of this analysis are presented.

Keywords

Data fusionIntelligent vehiclesADASLIDAR

Information

Type
Articles
Information
Robotica , Volume 30 , Issue 2 , March 2012 , pp. 185 - 193
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.http://www.darpa.mil/grandchallenge/Google Scholar
2.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. and Thrun, S., “Junior: The stanford entry in the urban challenge,” J. Field Robot. Ser. 9, 25, 569–597 (Sep. 2008).CrossRefGoogle Scholar
3.Urmson, C. et al. ., “Autonomous driving in urban environments: Boss and the urban challenge,” J. Field Robot. 25 (8), 425–466 (2008).CrossRefGoogle Scholar
4.Kammel, S., Ziegler, J., Pitzer, B., Werling, M., Gindele, T., Jagzent, D., Schröder, J., Thuy, M., Goebl, M., von Hundelshausen, F., Pink, O., Frese, C. and Stiller, C., “Team AnnieWAY's autonomous system for the 2007 DARPA urban challenge,” J. Field Robot. Ser. 9, 25, 615–639 (2008).CrossRefGoogle Scholar
5.Broggi, A., Cappalunga, A., Caraffi, C., Cattani, S., Ghidoni, S., Grisleri, P., Porta, P. P., Posterli, M., Zani, P. and Beck, J., “The Passive Sensing Suite of the TerraMax Autonomous Vehicle,” Proceedings of the IEEE Intelligent Vehicles Symposium 2008, Eindhoven, Netherlands (Jun. 2008), pp. 769–774.CrossRefGoogle Scholar
6.Fanping, Bu. and Chan, C.-Y., “Pedestrian Detection in Transit Bus Application: Sensing Technologies and Safety Solution,” Proceedings of the IEEE International Conference on Intelligent Vehicles (IV '05), Las Vegas, Nevada, USA (2005) pp. 100–105.Google Scholar
7.Langheim, J., Buchanan, A., Lages, U. and Wahl, M., “CARSENSE-New Environment Sensing for Advanced Driver Assistance Systems,” Proceedings of the International Conference on Intelligent Transportation Systems (ITSC '01), Torino, Italy (Apr. 2001) vol. 3, no. 2, pp. 796–801.Google Scholar
8.Gavrila, D. M, Kunert, M. and Lages, U., “A Multi-Sensor Approach for the Protection of Vulnerable Traffic Participants the PROTECTOR Project,” Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference (IMTC '01), Budapest, Hungary, (2001) vol. 3, pp. 2044–2048.Google Scholar
9.Wang, C., Thorpe, C. and Thrun, S., “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 the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003) pp. 842–849.Google Scholar
10.Moutarlier, P. and Chatila, R., “Stochastic Mutli-Sensor Data Fusion for Mobile Robot Location and Environment Modeling,” Proceedings of the 5th International Symposium on Robotic Research (1989) pp. 207–216.Google Scholar
11.Nashashibi, F. and Devy, M., “3D Incremental Modeling and Robot Localization using Laser Range Finder,” Proceedings of the IEEE International Conference Robotics and Automation (ICRA'93), Atlanta (May 2–7, 1993) pp. 20–27.Google Scholar
12.Früh, C. and Zakhor, A., “Fast 3D Model Generation in Urban Environment,” Proceedings of the International Conference on Multisensor Fusion and Integration of Intelligent Systems, Baden-Baden, Germany (2001) pp. 165–170.Google Scholar
13.Zao, H. and Shibasaki, R., “A vehicle-borne urban 3D acquisition system using single-row laser range scanner,” IEEE Trans. Syst., Man and Cybern. Part B: Cybern. 33 (4), 658–666 (2003).Google Scholar
14.Wang, C.-C., Thorpe, C. and Suppe, A., “Ladar-Based Detection and Tracking of Moving Objects from a Ground Vehicle at High Speeds,” Proceedings of the IEEE Intelligent Vehicles Symposium (IV '03) (Jun. 2003) pp. 416–421.Google Scholar
15.Wang, C.-C. and Thorpe, C., “A Hierarchical Object Based Representation for Simultaneous Localization and Mapping,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04) (Sep. 28–Oct. 2, 2004) vol. 1, pp. 412–418.Google Scholar
16.Rabbani, T. and van den Heuvel, F., “Efficient Hough Transform for Automatic Detection of Cylinders in Point Clouds,” Proceedings of the 11th Annual Conference of the Advanced School for Computing and Imaging (ASCI '05), Het Heijderbos, Heijen, The Netherlands (Jun. 2005) pp. 60–65.Google Scholar
17.Ahn, S. J., Effenberger, I., Roth-Koch, S. and Westkämper, E., “Geometric Segmentation and Object Recognition in Unordered and Incomplete Point Cloud,” Proceedings of the DAGM-Symposium (2003) pp. 450–457.Google Scholar
18.Thrun, S., “Learning occupancy grid maps with forward sensor models,” Auton. Robots 15 (2), 111–127 (2003).CrossRefGoogle Scholar
19.Tay, M., “An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments,” Int. J. Veh. Auton. Syst. 6 (17), 155–171 (Dec. 31, 2007).CrossRefGoogle Scholar
20.Hofmann, U., Rieder, A. and Dickmanns, E. D., “Radar and Vision Data Fusion for Hybrid Adaptive Cruise Control on Highways,” Proceedings of the International Conference Computing Visual Systems, Vancouver, BC, Canada (2001) pp. 125–138.Google Scholar
21.Kaempchen, N., Buehler, M. and Dietmayer, K., “Feature-Level Fusion for Free-Form Object Tracking using Laserscanner and Video,” Proceedings of the IEEE Intelligent Vehicles Symposium (Jun. 6–8, 2005) pp. 453–458.CrossRefGoogle Scholar
22.Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P. and Jung, H. G., “Localization and Analysis of Critical Areas in Urban Scenarios,” Proceedings of the IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherland (Jun. 4–6, 2008) pp. 1074–1079.Google Scholar
23.Premebida, C., Monteiro, G., Nunes, U. and Peixoto, P., “A Lidar and Vision-Based Approach for Pedestrian and Vehicle Detection and Tracking,” Proceedings of the IEEE International Conference on Intelligent Transportation Systems, Seattle, WA, USA (Sep.–Oct. 2007) pp. 1044–1049.Google Scholar
24.Hwang, J. P., Cho, S. E., Ryu, K. J., Park, S. and Kim, E., “Multi-Classifier Based LIDAR and Camera Fusion,” Proceedings of the IEEE International Conference on Intelligent Transportation Systems, Seattle, WA, USA (Sep.–Oct. 2007) pp. 467–472.Google Scholar
25.Milch, S. and Behrens, M., “Pedestrian Detection with Radar and Computer Vision,” Proceedings of the Conference on Progress in Automobile Lighting, Darmstadt, Germany (2001). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.9264Google Scholar
26.Zhao, H., Shao, X. W., Katabira, K. and Shibasaki, R., “Joint Tracking and Classification of Moving Objects at Intersection using a Single-Row Laser Range Scanner,” Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC '06), Canada (Sep. 17–20, 2006) pp. 287–294.Google Scholar
27.Sparbert, J., Dietmayer, K. and Streller, D.. “Lane Detection and Street Type Classification using Laser Range Images,” Proceedings of the IEEE Intelligent Transportation Systems Conference, Oakland, CA, USA (Aug. 25–29, 2001) pp. 454–459.Google Scholar
28.Fuerstenberg, K., Dietmayer, K. and Willhoeft, V., “Pedestrian Recognition in Urban Traffic Using a Vehicle Based Multilayer Laserscanner,” Proceedings of the Intelligent Vehicle Symposium (2002) pp. 31–35.Google Scholar
29.Streller, D., Furstenberg, K. and Dietmayer, K., “Vehicle and Object Models for Robust Tracking in Traffic Scenes Using Laser Range Images,” Proceedings of the IEEE Intelligent Transportation Systems (2002) pp. 118–123.Google Scholar
30.Fuerstenberg, K. Ch., Dietmayer, K. C. J., Eisenlauer, S. and Willhoeft, V., “Multilayer Laserscanner for robust Object Tracking and Classification in Urban Traffic Scenes,” Proceedings of the 9th World Congress on Intelligent Transport Systems (ITS '02), Chicago, (Oct. 2002) Paper 2054. pp. 7–8.Google Scholar
31.Fuerstenberg, K. Ch., Hipp, J. and Liebram, A. “A Laserscanner for Detailed Traffic Data Collection and Traffic Control,” Proceedings of the 7th World Congress on Intelligent Transport Systems (ITS '00), Turin, (2000) Paper 2335.Google Scholar
32.Gate, G. and Nashashibi, F., “Using Targets Appearance to Improve Pedestrian Classification with a Laser Scanner,” Proceedings of the IEEE Intelligent Vehicles Symposium (Jun. 2008) pp. 571–576.CrossRefGoogle Scholar
33.Nashashibi, F. and Bargeton, A., “Laser-Based Vehicles Tracking and Classification using Occlusion Reasoning and Confidence Estimation,” Proceedings of the IEEE Intelligent Vehicles Symposium (Jun. 2008) pp. 847–852.CrossRefGoogle Scholar
34.Ogawa, T. and Takagi, K., “Lane Recognition Using On-Vehicle LIDAR,” Proceedings of the IEEE Intelligent Vehicles Symposium (2008) pp. 540–545.Google Scholar
35.Mendes, A., Bento, L.C. and Nunes, U., “Multi-Target Detection and Tracking with a Laser Scanner,” Proceedings of the IEEE Intelligent Vehicles Symposium (Jun. 14–17, 2004) pp. 796–801.Google Scholar
36.Premebida, C. and Nunes, U.: “A Multi-Target Tracking and GMM Classifier for Intelligent Vehicles,” Proceedings of the IEEE Intelligent Transportation Systems Conference, Canada (2006) pp. 313–318.Google Scholar
37.MacLachlan, R. A. and Mertz, C., “Tracking of Moving Objects from a Moving Vehicle Using a Scanning Laser Rangefinder,” Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC '06) (Sep. 17–20, 2006) pp. 301–306.CrossRefGoogle Scholar
38.Fod, A., Howard, A. and Mataric, M, “A Laser-Based People Tracker,” Proceedings of the IEEE International Conference on Robotics and Automation (2002) pp. 3024–3029.Google Scholar
39.Schulz, D., Burgard, W., Fox, D. and Cremers, A. B., “Tracking Multiple Moving Targets with a Mobile Robot Using Particle Filters and Statistical Data Association,” Proceedings of the IEEE International Conference on Robotics and Automation (2001) vol. 2, pp. 1665–1670.Google Scholar
40.Bar-Shalom, Y. and Fortmann, T. E., “Tracking and Data Association (Mathematics in Science and Engineering) (Academic Press, San Diego, CA, USA, 1988).Google Scholar
41.Bar-Shalom, Y., “Tracking methods in a multitarget environment,” IEEE Trans. Autom. Control, 23 (4), (Aug. 1978).CrossRefGoogle Scholar
42.Bar-Shalom, Y. and Li, X.-R., Multitarget-Multisensor Tracking: Principles and Techniques (Yaakov Bar-Shalom, Danvers, MA, USA, 1995).Google Scholar
43.Blackman, S. and Popoli, R., Design and Analysis of Modern Tracking Systems (Artech House, MA, USA, 1999).Google Scholar
44.Reid, D. B., “An algorithm for tracking multiple targets,” IEEE Trans. Autom. Control, 24 (6), 843–854 (Dec. 1979).CrossRefGoogle Scholar
45.García, F., Cerri, P., Broggi, A., Amingol, J. M. and de la Escalera, A., “Vehicle Detection Based on Laser Radar” Proceedings of the European Conference on Computer Aided Systems Theory (EUROCAST '09) (Moreno-Diaz, R., Pichler, F. and Quesada-Arencibia, A., eds.). Lecture Notes In Computer Science, Vol. 5717. (Springer-Verlag, Berlin, Heidelberg, 2009) pp. 391–397.Google Scholar