LiDAR Based Real Time Multiple Vehicle Detection and Tracking
Self-driving vehicle require a high level of situational
awareness in order to maneuver safely when driving in real world
condition. This paper presents a LiDAR based real time perception
system that is able to process sensor raw data for multiple target
detection and tracking in dynamic environment. The proposed
algorithm is nonparametric and deterministic that is no assumptions
and priori knowledge are needed from the input data and no
initializations are required. Additionally, the proposed method is
working on the three-dimensional data directly generated by LiDAR
while not scarifying the rich information contained in the domain of
3D. Moreover, a fast and efficient for real time clustering algorithm
is applied based on a radially bounded nearest neighbor (RBNN).
Hungarian algorithm procedure and adaptive Kalman filtering are
used for data association and tracking algorithm. The proposed
algorithm is able to run in real time with average run time of 70ms
per frame.
[1] Manvi Malik and S.Majumder. An integrated computer vision based
approach for driving assistance to enhance visibility in all weather
conditions. In International and National Conference on Machines and
Mechanisms, Roorkee,India, December 2013.
[2] Daniel Gohring, Miao Wang, Michael Schnurmacher, and Tinosch
Ganjineh. Radar/lidar sensor fusion for car-following on highways. In
Automation, Robotics and Applications (ICARA), 2011 5th International
Conference on, pages 407–412, Wellington, New Zealand, December
2011. IEEE.
[3] Karsten Bohlmann Stefan Laible, Yasir Niaz Khan and Andreas Zell.
3d lidar- and camera-based terrain classification under different lighting
conditions. Autonomous Mobile Systems 2012, pages 21–29, 2012.
[4] S.Kammel, J.Ziegler, B.Pitzer, M.Werling, T.Gindele, D.Jagzent,
J.Schroder, M.Thuy, M.Godel, F.von Hundelshausen, O.Pink, C.Frese,
and C.Stiller. Team annieway’s autonomous system for the darpa urban
challenge 2007. 2008.
[5] M.Montemerlo, J.Becker, S.Bhat, H.Dahlkamp, D.Dolgov, S.Ettinger,
D.Haehnel, T.Hilden, G.Hoffmann, B.Huhnke, D.Johnston, S.Klumpp,
D.Langer, A.Levandowski, J.Levinson, J.Marcil, D.Orenstein, J.Paefgen,
I.Penny, A.Petrovskaya, M.Pflueger, G.Stanek, D.Stavens, A.Vogot, and
S.Thrun. Junior: The stanford entry in the urban challenge. 8:569–597,
September 2008.
[6] Frank Moosmann, Oliver Pink, and Christoph Stiller. Segmentation of
3d lidar data in non-flat urban environments using a local convexity
criterion. In Intelligent Vehicles Symposium, 2009 IEEE, pages 215–220,
Xi’an,China, June 2009. IEEE.
[7] B.Douillard, J.Underwood, N.Kuntz, V.Vlaskine, A.Quadros, P.Morton,
and A.Frenkel. On the segmentation of 3d lidar point clouds. In Robotics
and Automation (ICRA), 2011 IEEE International Conference, pages
2798–2805, Shanghai,China, May 2011. IEEE.
[8] Klass Klasing, Dirk Wollherr, and Martin Buss. A clustering method
for efficient segmentation of 3d laser data. In Robotics and Automation,
2008. ICRA 2008. IEEE International Conference.
[9] Liang Zhang, Qingquan Li, Ming Li, Qingzhou Mao, and Andreas
Nuchter. Multiple vehicle-like target tracking based on the velodyne
lidar. IFAC Intelligent Autonomous Vehicles Symposium, 8:126–131,
2013.
[10] R. E. Kalman. Ba new approach to linear filtering and prediction
problems. In Trans. ASMEVJ. Basic Eng, pages ser.D, vol. 82, pp.3545,
1960.
[11] Karen Schuckman. Introduction to lasers and lidar. https://www.
e-education.psu.edu/geog481/l1 p3.html. Accessed: 2015-10-13.
[12] Andrew W. Moore. An intoductory tutorial on kd-trees. Technical Report
No. 209, Computer Laboratory,University of Cambridge, 1991.
[13] Daniel Dworak. 3d points cloud reduction using modified k-d tree
method. In VWyjazdowa Sesja Naukowa Doktorantw Politechniki dzkiej,
2015.
[14] S. Thrun. Stanley: The robot that won the darpa grand challenge. In
Journal of Filed Robot (JFR).
[15] M. Betke. Tracking large variable numbers of objects in clutter. In
Computer Vision and Pattern Recognition, CVPR ’07. IEEE Conference
on.
[16] Adrian Macaveiu, Andrei Campeanu, and Ioan Nafornita. Kalman-based
tracker for multiple radar targets. In COMM 2014 International
Conference on Communications, Bucharest,Romania, May 2014.
[1] Manvi Malik and S.Majumder. An integrated computer vision based
approach for driving assistance to enhance visibility in all weather
conditions. In International and National Conference on Machines and
Mechanisms, Roorkee,India, December 2013.
[2] Daniel Gohring, Miao Wang, Michael Schnurmacher, and Tinosch
Ganjineh. Radar/lidar sensor fusion for car-following on highways. In
Automation, Robotics and Applications (ICARA), 2011 5th International
Conference on, pages 407–412, Wellington, New Zealand, December
2011. IEEE.
[3] Karsten Bohlmann Stefan Laible, Yasir Niaz Khan and Andreas Zell.
3d lidar- and camera-based terrain classification under different lighting
conditions. Autonomous Mobile Systems 2012, pages 21–29, 2012.
[4] S.Kammel, J.Ziegler, B.Pitzer, M.Werling, T.Gindele, D.Jagzent,
J.Schroder, M.Thuy, M.Godel, F.von Hundelshausen, O.Pink, C.Frese,
and C.Stiller. Team annieway’s autonomous system for the darpa urban
challenge 2007. 2008.
[5] M.Montemerlo, J.Becker, S.Bhat, H.Dahlkamp, D.Dolgov, S.Ettinger,
D.Haehnel, T.Hilden, G.Hoffmann, B.Huhnke, D.Johnston, S.Klumpp,
D.Langer, A.Levandowski, J.Levinson, J.Marcil, D.Orenstein, J.Paefgen,
I.Penny, A.Petrovskaya, M.Pflueger, G.Stanek, D.Stavens, A.Vogot, and
S.Thrun. Junior: The stanford entry in the urban challenge. 8:569–597,
September 2008.
[6] Frank Moosmann, Oliver Pink, and Christoph Stiller. Segmentation of
3d lidar data in non-flat urban environments using a local convexity
criterion. In Intelligent Vehicles Symposium, 2009 IEEE, pages 215–220,
Xi’an,China, June 2009. IEEE.
[7] B.Douillard, J.Underwood, N.Kuntz, V.Vlaskine, A.Quadros, P.Morton,
and A.Frenkel. On the segmentation of 3d lidar point clouds. In Robotics
and Automation (ICRA), 2011 IEEE International Conference, pages
2798–2805, Shanghai,China, May 2011. IEEE.
[8] Klass Klasing, Dirk Wollherr, and Martin Buss. A clustering method
for efficient segmentation of 3d laser data. In Robotics and Automation,
2008. ICRA 2008. IEEE International Conference.
[9] Liang Zhang, Qingquan Li, Ming Li, Qingzhou Mao, and Andreas
Nuchter. Multiple vehicle-like target tracking based on the velodyne
lidar. IFAC Intelligent Autonomous Vehicles Symposium, 8:126–131,
2013.
[10] R. E. Kalman. Ba new approach to linear filtering and prediction
problems. In Trans. ASMEVJ. Basic Eng, pages ser.D, vol. 82, pp.3545,
1960.
[11] Karen Schuckman. Introduction to lasers and lidar. https://www.
e-education.psu.edu/geog481/l1 p3.html. Accessed: 2015-10-13.
[12] Andrew W. Moore. An intoductory tutorial on kd-trees. Technical Report
No. 209, Computer Laboratory,University of Cambridge, 1991.
[13] Daniel Dworak. 3d points cloud reduction using modified k-d tree
method. In VWyjazdowa Sesja Naukowa Doktorantw Politechniki dzkiej,
2015.
[14] S. Thrun. Stanley: The robot that won the darpa grand challenge. In
Journal of Filed Robot (JFR).
[15] M. Betke. Tracking large variable numbers of objects in clutter. In
Computer Vision and Pattern Recognition, CVPR ’07. IEEE Conference
on.
[16] Adrian Macaveiu, Andrei Campeanu, and Ioan Nafornita. Kalman-based
tracker for multiple radar targets. In COMM 2014 International
Conference on Communications, Bucharest,Romania, May 2014.
@article{"International Journal of Information, Control and Computer Sciences:73156", author = "Zhongzhen Luo and Saeid Habibi and Martin v. Mohrenschildt", title = "LiDAR Based Real Time Multiple Vehicle Detection and Tracking", abstract = "Self-driving vehicle require a high level of situational
awareness in order to maneuver safely when driving in real world
condition. This paper presents a LiDAR based real time perception
system that is able to process sensor raw data for multiple target
detection and tracking in dynamic environment. The proposed
algorithm is nonparametric and deterministic that is no assumptions
and priori knowledge are needed from the input data and no
initializations are required. Additionally, the proposed method is
working on the three-dimensional data directly generated by LiDAR
while not scarifying the rich information contained in the domain of
3D. Moreover, a fast and efficient for real time clustering algorithm
is applied based on a radially bounded nearest neighbor (RBNN).
Hungarian algorithm procedure and adaptive Kalman filtering are
used for data association and tracking algorithm. The proposed
algorithm is able to run in real time with average run time of 70ms
per frame.", keywords = "LiDAR, real-time system, clustering, tracking, data
association.", volume = "10", number = "6", pages = "1135-8", }
