Real Time Lidar and Radar High-Level Fusion for Obstacle Detection and Tracking with Evaluation on a Ground Truth

Both Lidars and Radars are sensors for obstacle
detection. While Lidars are very accurate on obstacles positions
and less accurate on their velocities, Radars are more precise on
obstacles velocities and less precise on their positions. Sensor
fusion between Lidar and Radar aims at improving obstacle
detection using advantages of the two sensors. The present
paper proposes a real-time Lidar/Radar data fusion algorithm
for obstacle detection and tracking based on the global nearest
neighbour standard filter (GNN). This algorithm is implemented
and embedded in an automative vehicle as a component generated
by a real-time multisensor software. The benefits of data fusion
comparing with the use of a single sensor are illustrated through
several tracking scenarios (on a highway and on a bend) and
using real-time kinematic sensors mounted on the ego and tracked
vehicles as a ground truth.

Authors:

• Hatem Hajri
• Mohamed-Cherif Rahal

Keywords:

• Ground truth
• Hungarian algorithm
• lidar Radar data fusion
• global nearest neighbor filter.

References:

[1] D. L. Hall and S. A. H. McMullen, Mathematical Techniques in
Multisensor Data Fusion. Artech House, 2004.
[2] H. B. Mitchell, Multi-Sensor Data Fusion: An Introduction, 1st ed.
Springer Publishing Company, Incorporated, 2007.
[3] Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association,
ser. Mathematics in Science and Engineering. Academic Press, 1988,
no. 179.
[4] C. Blanc, B. T. Le, Y. Le, and G. R. Moreira, “Track to track
fusion method applied to road obstacle detection,” IEEE International
Conference on Information Fusion, 2004.
[5] C. Blanc, P. Checchin, S. Gidel, and L. Trassoudaine, “Data
fusion performance evaluation for range measurements combine with
cartesian ones for road obstacle tracking,” 11th IEEE International
Conference on Intelligent Transportation Systems, 2007.
[6] D. Gohring, M. Wang, M. Schnurmacher, and T. Ganjineh,
“Radar/lidar sensor fusion for car-following on highways,” IEEE Int.
Conf. Autom. Robot. Appl., pp. 407–412, 2011.
[7] R. O. Chavez-Garcia and O. Aycard, “Multiple Sensor Fusion and
Classification for Moving Object Detection and Tracking,” IEEE
Transactions on Intelligent Transportation Systems, vol. 17, no. 2, pp.
252–534, 2015.
[8] A. Rangesh and M. M. Trivedi, “No blind spots: Full-surround
multi-object tracking for autonomous vehicles using Cameras and
Lidars,” https://arxiv.org/pdf/1802.08755.pdf., 2018.
[9] H. Durrant-Whyte, Introduction to estimation and the Kalman filter.
Australian Centre for Fields Robotics.
[10] F. Nashashibi, “Rtm@ps: a framework for prototyping automotive
multi-sensor applications,” pp. 99 – 103, 02 2000.
[11] J. Munkres, “Algorithms for the assignment and transportation
problems,” Journal of the Society of Industrial and Applied
Mathematics, vol. 5, no. 1, pp. 32–38, March 1957.