[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Vehicle specific robust traversability indices using roadmaps on 3D pointclouds

  • Regular Paper
  • Published:
International Journal of Intelligent Robotics and Applications Aims and scope Submit manuscript

Abstract

Roads undergo rapid deterioration due to various economic, social and natural reasons. The uncertainty and unavailability of road safety information has become a major issue in reliably transporting goods, movement of heavy machinery, transport of people and materials to/from disaster areas and in even giving plain guarantees for safe traversability of a road that is good on paper. In this paper, we propose a novel approach towards reducing this uncertainty. Instead of asking qualitative questions about whether a road network is generally traversable for a general class of vehicles, we propose a solution that gives precise quantitative answers as a distance modulated roadmap and confirms whether a particular segment of the road is traversable for a particular type of vehicle. In this paper, we extend on our previous work Khan et al. (2016) of traversability analysis by empirically proving that our framework is robust to shape and size of the roadmap graph. We show that obstacle size, count, and location can have varying effects on traversability of different vehicles. We also propose a new Road Safety Index (RSI) which is an extension of our previously proposed index, Road Traversability Index (RTI). RSI takes into account multiple disjoint narrow passages that belong to the same connected component of the graph. Lastly, we present application scenarios where our roadmap based traversability can be used to assess the safety of the road for a particular vehicle.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. CARMERA (https://www.carmera.com) and Civil Maps (https://civilmaps.com)

  2. In this work, road safety refers to a measure of traversable collision free space on the road. A road with more free space for a vehicle is safer than a road with less space. Hence, if an obstacle is introduced, it would decrease road safety if it hinders the vehicle motion.

  3. Unique paths are paths in a graph that do not share common nodes. Maxflow/Mincut can be used to compute it.

  4. To incorporate dynamic obstacles, we will have to model the obstacle motion and predict its location based on the time when vehicle reach the obstacle.

  5. We do see slight variation of the graph from bell shape for \(20\%\) graph size in Fig. 12 and we believe it is because of the very sparse nature of the graph. It is also evident in the boxplot in Fig. 11 where variation in RTI for graph size of \(20\%\) is very high.

  6. By collision free it is meant that even if an obstacle is present on the road but it is not hindering the vehicle motion then it is still considered collision free space.

  7. Space and paths are used interchangeably because paths approximately represent traversable space.

References

  • Abbas, S.M., Muhammad, A., Mehdi, S.A., Berns, K.: Improvements in accuracy of single camera terrain classification. In: 16th International Conference on Advanced Robotics (ICAR), 2013, pp. 1–6. IEEE (2013)

  • Agarwal, P.K., Fox, K., Salzman, O.: An efficient algorithm for computing high-quality paths amid polygonal obstacles. ACM Trans. Algorithms (TALG) 14(4), 46 (2018)

    MathSciNet  MATH  Google Scholar 

  • Angelova, A., Matthies, L., Helmick, D., Perona, P.: Learning and prediction of slip from visual information. J. Field Robot. 24(3), 205–231 (2007)

    Article  Google Scholar 

  • Berczi, L.P., Posner, I., Barfoot, T.D.: Learning to assess terrain from human demonstration using an introspective gaussian-process classifier. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 3178–3185. IEEE (2015)

  • Bhattacharya, P., Gavrilova, M.L.: Roadmap-based path planning-using the voronoi diagram for a clearance-based shortest path. IEEE Robotics & Automation Magazine 15(2), (2008)

  • Bonnafous, D., Lacroix, S., Siméon, T.: Motion generation for a rover on rough terrains. In: Intelligent robots and systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on, vol. 2, pp 784–789. IEEE (2001)

  • Bounini, F., Gingras, D., Pollart, H., Gruyer, D.: Modified artificial potential field method for online path planning applications. In: Intelligent Vehicles Symposium (IV), 2017 IEEE, pp 180–185. IEEE (2017)

  • Brooks, C., Iagnemma, K., Dubowsky, S.: Vibration-based terrain analysis for mobile robots. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005., pp 3415–3420. IEEE (2005)

  • Buehler, M., Iagnemma, K., Singh, S.: The 2005 DARPA grand challenge: the great robot race, vol. 36. Springer Science & Business Media (2007)

  • Caltagirone, L., Bellone, M., Svensson, L., Wahde, M.: Lidar-based driving path generation using fully convolutional neural networks. In: Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on, pp 1–6. IEEE (2017)

  • Carrington, P.J., Scott, J., Wasserman, S.: Models and Methods in Social Network Analysis, vol. 28. Cambridge University Press, Cambridge (2005)

    Book  Google Scholar 

  • Chen, C., Seff, A., Kornhauser, A., Xiao, J.: Deepdriving: Learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2722–2730 (2015)

  • Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv preprint arXiv:1606.00915 (2016)

  • Choset, H.M., Hutchinson, S., Lynch, K.M., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of robot motion: theory, algorithms, and implementation. MIT press (2005)

  • Coll, B., Moutari, S., Marshall, A.: Hotspots identification and ranking for road safety improvement: an alternative approach. Accident Anal. Prevention 59C, 604–617 (2013)

    Article  Google Scholar 

  • Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: The IEEE Conference on Ccomputer vision and Pattern Recognition (CVPR) (2016)

  • Cremers, D.: Direct methods for 3d reconstruction and visual slam. In: Machine vision applications (MVA), 2017 fifteenth IAPR international conference on, pp. 34–38. IEEE (2017)

  • Devaurs, D., Siméon, T., Cortés, J.: Efficient sampling-based approaches to optimal path planning in complex cost spaces. In: Algorithmic Foundations of Robotics XI, pp. 143–159. Springer (2015)

  • Edelkamp, S., Schrödl, S.: Route planning and map inference with global positioning traces. Computer Science in Perspective pp 128–151 (2003)

  • Elvik, R., Vaa, T., Erke, A., Sorensen, M.: The handbook of road safety measures. Emerald Group Publishing (2009)

  • Ettlin, A., Bleuler, H.: Rough-terrain robot motion planning based on obstacleness. In: Control, Automation, Robotics and Vision, 2006. ICARCV’06. 9th International Conference on, pp. 1–6. IEEE (2006)

  • Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., Song, D.: Robust physical-world attacks on machine learning models. ArXiv e-prints (2017)

  • Fancello, G., Carta, M., Fadda, P.: A decision support system for road safety analysis. Trans. Res. Proc. 5, 201–210 (2015)

    Google Scholar 

  • Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., González, M.C.: Safe driving using mobile phones. IEEE Trans. Intell. Trans. Syst. 13(3), 1462–1468 (2012)

    Article  Google Scholar 

  • Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robotics Res. (IJRR) (2013)

  • Gennery, D.B.: Traversability analysis and path planning for a planetary rover. Autonomous Robots 6(2), 131–146 (1999)

    Article  Google Scholar 

  • Geraerts, R., Overmars, M.H.: Clearance based path optimization for motion planning. In: Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 IEEE International Conference on, vol. 3, pp. 2386–2392. IEEE (2004)

  • Gregoriades, A., Chrystodoulides, A.: Extracting traffic safety knowledge from historical accident data. In: Adjunct Proceedings of the 14th International Conference on Location Based Services, pp. 109–114. ETH Zurich (2018)

  • Hackel, T., Wegner, J.D., Schindler, K.: Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Ann Photogrammetry Remote Sens Spatial Inform Sci 3(3) (2016)

  • Haenggi, M., Andrews, J.G., Baccelli, F., Dousse, O., Franceschetti, M.: Stochastic geometry and random graphs for the analysis and design of wireless networks. IEEE J. Selected Areas Commun. 27(7), (2009)

  • Hayat, M., Muhammad, A.: Spectral properties of expansive configuration spaces: An empirical study. In: Robotics and Automation (ICRA), 2011 IEEE International Conference on, pp. 4474–4479. IEEE (2011)

  • Hirose, N., Sadeghian, A., Goebel, P., Savarese, S.: To go or not to go? a near unsupervised learning approach for robot navigation. arXiv preprint arXiv:1709.05439 (2017)

  • Howard, A., Seraji, H.: Vision-based terrain characterization and traversability assessment. J. Robotic Syst. 18(10), 577–587 (2001)

    Article  Google Scholar 

  • Hubschneider, C., Bauer, A., Doll, J., Weber, M., Klemm, S., Kuhnt, F., Zöllner, J.M.: Integrating end-to-end learned steering into probabilistic autonomous driving. In: Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on, pp. 1–7. IEEE (2017)

  • Iagnemma, K., Shibly, H., Dubowsky, S.: On-line terrain parameter estimation for planetary rovers. In: Robotics and Automation, 2002. Proceedings. ICRA’02. IEEE International Conference on, vol. 3, pp. 3142–3147. IEEE (2002)

  • IEEE-Spectrum: Will google self-driving cars be limited by “map anxiety”? http://spectrum.ieee.org/cars-that-think/transportation/self-driving/will-consumers-interested-in-googles-selfdriving-cars-suffer-from-map-anxiety (2014)

  • IEEE-Spectrum: Fatal tesla self-driving car crash reminds us that robots aren’t perfect. http://spectrum.ieee.org/cars-that-think/transportation/self-driving/fatal-tesla-autopilot-crash-reminds-us-that-robots-arent-perfect (2016)

  • IEEE-Spectrum: Tesla’s massive new autopilot update is released, promising safer driving. http://spectrum.ieee.org/cars-that-think/transportation/self-driving/teslas-massive-new-autopilot-update-is-released (2016)

  • Ishigami, G., Nagatani, K., Yoshida, K.: Path planning and evaluation for planetary rovers based on dynamic mobility index. In: Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pp. 601–606. IEEE (2011)

  • Jaillet, L., Cortés, J., Siméon, T.: Sampling-based path planning on configuration-space costmaps. IEEE Trans. Robot. 26(4), 635–646 (2010)

    Article  Google Scholar 

  • Khaleghian, S., Taheri, S.: Terrain classification using intelligent tire. J. Terramech. 71, 15–24 (2017)

    Article  Google Scholar 

  • Khan, M.M., Ali, H., Berns, K., Muhammad, A.: Road traversability analysis using network properties of roadmaps. In: Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pp. 2960–2965. IEEE (2016)

  • Khan, Y.N., Komma, P., Zell, A.: High resolution visual terrain classification for outdoor robots. In: Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pp. 1014–1021. IEEE (2011)

  • Kovács, B., Szayer, G., Tajti, F., Burdelis, M., Korondi, P.: A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robot. Auto. Syst. 82, 24–34 (2016)

    Article  Google Scholar 

  • Krüsi, P., Furgale, P., Bosse, M., Siegwart, R.: Driving on point clouds: motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments. J. Field Robot. 34(5), 940–984 (2017)

    Article  Google Scholar 

  • Larson, J., Trivedi, M., Bruch, M.: Off-road terrain traversability analysis and hazard avoidance for ugvs. CALIFORNIA UNIV SAN DIEGO DEPT OF ELECTRICAL ENGINEERING, Tech. rep. (2011)

  • Lee, J., Mottaghi, R., Pippin, C., Balch, T.: Graph-based planning using local information for unknown outdoor environments. In: Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, pp. 1455–1460. IEEE (2009)

  • Levi, D., Garnett, N., Fetaya, E., Herzlyia, I.: Stixelnet: A deep convolutional network for obstacle detection and road segmentation. In: BMVC, pp. 109–1 (2015)

  • Manduchi, R., Castano, A., Talukder, A., Matthies, L.: Obstacle detection and terrain classification for autonomous off-road navigation. Auto. Robot. 18(1), 81–102 (2005)

    Article  Google Scholar 

  • Martin, S., Corke, P.: Long-term exploration & tours for energy constrained robots with online proprioceptive traversability estimation. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp. 5778–5785. IEEE (2014)

  • Milella, A., Reina, G., Underwood, J.: A self-learning framework for statistical ground classification using radar and monocular vision. J. Field Robot. 32(1), 20–41 (2015)

    Article  Google Scholar 

  • Naidoo, T., Joubert, D., Chiwewe, T., Tyatyantsi, A., Rancati, B., Mbizeni, A.: Visual surveying platform for the automated detection of road surface distresses. In: Sensors, MEMS and Electro-Optical Systems, vol. 9257, p. 92570D. International Society for Optics and Photonics (2014)

  • Newcombe, R.: Dense visual slam. Ph.D. thesis, Imperial College London (2012)

  • Ojeda, L., Borenstein, J., Witus, G., Karlsen, R.: Terrain characterization and classification with a mobile robot. J. Field Robot. 23(2), 103–122 (2006)

    Article  Google Scholar 

  • Papadakis, P.: Terrain traversability analysis methods for unmanned ground vehicles: A survey. Eng. Appl. Artificial Intell. 26(4), 1373–1385 (2013)

    Article  Google Scholar 

  • Papadakis, P., Pirri, F.: 3d mobility learning and regression of articulated, tracked robotic vehicles by physics-based optimization. In: Virtual Reality Interaction and Physical Simulation, Eurographics (2012)

  • Park, B., Choi, J., Chung, W.K.: Sampling-based retraction method for improving the quality of mobile robot path planning. Int. J. Control Auto. Syst. 10(5), 982–991 (2012)

    Article  Google Scholar 

  • Prieto, F., Gómez-Déniz, E., Sarabia, J.M.: Modelling road accident blackspots data with the discrete generalized pareto distribution. Accident Anal. Prevention 71, 38–49 (2014)

    Article  Google Scholar 

  • Rajamani, R.: Vehicle dynamics and control. Springer Science & Business Media, Berlin (2011)

    MATH  Google Scholar 

  • Royer, L., Reimann, M., Andreopoulos, B., Schroeder, M.: Unraveling protein networks with power graph analysis. PLoS Comput Biol. 4(7), e1000108 (2008)

    Article  MathSciNet  Google Scholar 

  • Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal Mach. Intell. 39(4), 640–651 (2017)

    Article  Google Scholar 

  • Siméon, T.: Motion planning for a non-holonomic mobile robot on 3-dimensional terrains. In: Geometric reasoning for perception and action, pp. 38–50. Springer (1993)

  • Singh, S., Simmons, R., Smith, T., Stentz, A., Verma, V., Yahja, A., Schwehr, K.: Recent progress in local and global traversability for planetary rovers. In: Robotics and Automation, 2000. Proceedings. ICRA’00. IEEE International Conference on, vol. 2, pp 1194–1200. IEEE (2000)

  • Sock, J., Kim, J., Min, J., Kwak, K.: Probabilistic traversability map generation using 3d-lidar and camera. In: Robotics and Automation (ICRA), 2016 IEEE International Conference on, pp. 5631–5637. IEEE (2016)

  • Suger, B., Steder, B., Burgard, W.: Traversability analysis for mobile robots in outdoor environments: A semi-supervised learning approach based on 3d-lidar data. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 3941–3946. IEEE (2015)

  • Tahirovic, A., Magnani, G.: A roughness-based rrt for mobile robot navigation planning. IFAC Proc. Vol. 44(1), 5944–5949 (2011)

    Article  Google Scholar 

  • Takemura, R., Ishigami, G.: Traversability-based RRT* for planetary rover path planning in rough terrain with lidar point cloud data. Journal of Robotics and Mechatronics 29(5), 838–846 (2017)

  • Tchapmi, L.P., Choy, C.B., Armeni, I., Gwak, J., Savarese, S.: SEGCloud: Semantic Segmentation of 3D Point Clouds. ArXiv e-prints (2017)

  • Tompkins, P., Stentz, A., Wettergreen, D.: Global path planning for mars rover exploration. In: Aerospace Conference, 2004. Proceedings. 2004 IEEE, vol. 2, pp. 801–815. IEEE (2004)

  • Vandapel, N., Donamukkala, R.R., Hebert, M.: Unmanned ground vehicle navigation using aerial ladar data. Int. J. Robot. Res. 25(1), 31–51 (2006)

    Article  Google Scholar 

  • Yamada, M., Ueda, K., Horiba, I., Sugie, N.: Discrimination of the road condition toward understanding of vehicle driving environments. IEEE Trans. Intell. Trans. Syst. 2(1), 26–31 (2001)

    Article  Google Scholar 

  • Zakeri, H., Nejad, F.M., Fahimifar, A.: Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Archives of Computational Methods in Engineering pp. 1–43 (2016)

  • Zhang, M., Chen, C., Wo, T., Xie, T., Bhuiyan, M.Z.A., Lin, X.: Safedrive: online driving anomaly detection from large-scale vehicle data. IEEE Trans. Indu. Inform. 13(4), 2087–2096 (2017)

    Article  Google Scholar 

  • Ziegel, E.R.: Statistical inference (2002)

Download references

Author information

Authors and Affiliations

  1. Syed Babar Ali School of Science and Engineering, LUMS, Lahore, Pakistan

    Muhammad Mudassir Khan

  2. Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany

    Karsten Berns

  3. Syed Babar Ali School of Science and Engineering, LUMS, Lahore, Pakistan

    Abubakr Muhammad

Authors
  1. Muhammad Mudassir Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karsten Berns
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abubakr Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Mudassir Khan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 2300 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, M.M., Berns, K. & Muhammad, A. Vehicle specific robust traversability indices using roadmaps on 3D pointclouds. Int J Intell Robot Appl 4, 490–506 (2020). https://doi.org/10.1007/s41315-020-00148-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41315-020-00148-x

Keywords

Search

Navigation