Technical Papers
Aug 2, 2017

Nonurban Driver Assistance with 2D Tilting Laser Reconstruction

Publication: Journal of Surveying Engineering
Volume 143, Issue 4

Abstract

In rough environments, such as off-road or post-crisis environments, drivers often need assistance in piloting their vehicles, especially to anticipate obstacles on the driving path. This research aimed to develop such a system, focusing on a cheap and simple method for three-dimensional (3D) reconstruction. This step is important in the detection and classification of negative (under-the-ground) and positive (above-the-ground) obstacles. This information can then be exploited to give feedback to the driver, hence achieving the goal of driver assistance. This article focuses on the 3D reconstruction algorithm, its implementation, and its experimental testing. The choice of sensors is first explained. Because the approach is designed for driver assistance, not for mapping purposes, this leads to real-time and operational constraints (robust and cheap sensors) but eliminates certain other constraints (e.g., dealing with large point clouds). Thus, the selected sensors are a fusion of tilting two-dimensional (2D) LIDAR and stereo cameras. The three-step reconstruction algorithm is then explained. First, the system gets odometry from a stereo pair. Second, 3D points are computed in the ego reference frame with 2D LIDAR scans and servomotor rotation angles. Third, the 3D points are placed in the world reference frame by regularly positioning the previous points on a linear path given by the odometry measurements. The result is a 3D point cloud of the environment in front of the vehicle. Finally, the experimental validation of the approach is explained. A small mobile robot was first tested before applying the approach to a vehicle. Ground-truth acquisitions were conducted to test the veracity of the approach in an outdoor environment. Results show that a coherent map is obtained, but this fusion is not yet suitable for off-road driving at high speeds. However, with some improvements in visual odometry, good 3D reconstruction can be obtained for low- and high-speed driving.

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Acknowledgments

This work is made in partnership with a French armored-vehicle manufacturer (Nexter Systems). Safety of vehicle operators is one of its main research topics. The extreme example considered herein can be generalized to other missions, such as post-crisis management.

References

Braman, T., and Grossman, O. (2006). “Designing vibration and shock isolation systems for micro electrical machined based inertial measurement units.” Proc., 2006 IEEE/ION Position Location and Navigation Symp., IEEE, New York, 400–404.
Broggi, A., et al. (2010). “Terramax vision at the urban challenge 2007.” IEEE Trans. Intell. Transp. Syst., 11(1), 194–205.
Cappalunga, A., Cattani, S., Broggi, A., McDaniel, M. S., and Dutta, S. (2010). “Real-time 3D terrain elevation mapping using ants optimization algorithm and stereo vision.” Proc., 2010 IEEE Intelligent Vehicles Symp., IEEE, New York, 902–909.
Cole, D. M., and Newman, P. M. (2006). “Using laser range data for 3D SLAM in outdoor environments.” Proc., 2006 IEEE Int. Conf. on Robotics and Automation, IEEE, New York, 1556–1563.
DARPA (Defense Advanced Research Projects Agency). (2005). “Grand challenge ’05.” 〈http://archive.darpa.mil/grandchallenge05/〉.
Espino, J. C., Steux, B., and El Hamzaoui, O. (2011). “Safe navigating system for indoor environments.” Proc., 5th Int. Conf. on Automation, Robotics, and Applications, IEEE, New York, 419–423.
Fischler, M. A., and Bolles, R. C. (1981). “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography.” Commun. ACM, 24(6), 381–395.
Geiger, A., Ziegler, J., and Stiller, C. (2011). “StereoScan: Dense 3D reconstruction in real-time.” Proc., 2011 IEEE Intelligent Vehicles Symp., IEEE, New York, 963–968.
Kitt, B., Geiger, A., and Lategahn, H. (2010). “Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme.” Proc., 2010 IEEE Intelligent Vehicles Symp., IEEE, New York, 486–492.
Langley, R. B. (1998). “RTK GPS.” GPS World, 70–75. 〈http://www2.unb.ca/gge/Resources/gpsworld.september98.pdf〉.
Larson, J., and Trivedi, M. (2011). “LIDAR based off-road negative obstacle detection and analysis.” Proc., 14th IEEE Int. Conf. on Intelligent Transportation Systems, IEEE, New York.
Malartre, F., Delmas, P., Chapuis, R., and Debain, C. (2010). “Real-time dense digital elevation map estimation using laserscanner and camera SLAM process.” Proc., 11th Int. Conf. on Control, Automation, Robotics, and Vision, IEEE, New York, 1212–1218.
Matthies, L., and Rankin, A. (2003). “Negative obstacle detection by thermal signature.” Proc., 2003 IEEE Int. Conf. on Intelligent Robots and Systems, IEEE, New York.
Piatti, D. (2010). “Time-of-flight cameras: Tests, calibration and multi-frame registration for automatic 3D object reconstruction.” Ph.D. thesis, Dept. of Environment and Territory, Polytechnic Univ. of Turin, Turin, Italy.
Sibley, G., Sukhatme, G. S., and Matthies, L. H. (2006). “The iterated Sigma point Kalman filter with applications to long range stereo.” Proc., Robotics: Science and Systems II, Robotics Science and Systems Foundation, Berlin.
Thrun, S., et al. (2006). “Stanley: The robot that won the DARPA Grand Challenge.” J. Field Rob., 23(9), 661–692.
Tian, T. Y., Tomasi, C., and Heeger, D. J. (1996). “Comparison of approaches to egomotion computation.” Proc., 1996 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, IEEE, New York.
Wu, E.-Y., Li, G.-Y., Xiang, Z.-Y., and Liu, J.-L. (2008). “Stereo vision based SLAM using Rao-Blackwellised particle filter.” J. Zhejiang Univ., Sci. A, 9(4), 500–509.
Zhang, J., and Singh, S. (2014). “LOAM: Lidar odometry and mapping in real-time.” 〈http://ri.cmu.edu/pub_files/2014/7/Ji_LidarMapping_RSS2014_v8.pdf〉.

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Go to Journal of Surveying Engineering
Journal of Surveying Engineering
Volume 143Issue 4November 2017

History

Received: Mar 16, 2016
Accepted: Mar 29, 2017
Published online: Aug 2, 2017
Published in print: Nov 1, 2017
Discussion open until: Jan 2, 2018

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Authors

Affiliations

Bruno Ricaud [email protected]
Ph.D. Student, MINES ParisTech, PSL Research Univ., Centre for Robotics, 60 Blvd. St. Michel, Paris 75006, France (corresponding author). E-mail: [email protected]
Lecturer, MINES ParisTech, PSL Research Univ., Centre for Robotics, 60 Blvd. St. Michel, Paris 75006, France. E-mail: [email protected]
Arnaud de La Fortelle [email protected]
Head of Laboratory and Professor, MINES ParisTech, PSL Research Univ., Centre for Robotics, 60 Blvd. St. Michel, Paris 75006, France. E-mail: [email protected]

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