Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Home
Hostname: page-component-684899dbb8-67wsf Total loading time: 0.306 Render date: 2022-05-27T07:22:45.412Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }
  • English
Robotica Robotica

Article contents

Detection and Tracking of Moving Obstacles (DATMO): A Review

Published online by Cambridge University Press:  12 July 2019

Ángel Llamazares ,
Eduardo J. Molinos  and
Manuel Ocaña Open the ORCID record for Manuel Ocaña[Opens in a new window]
Ángel Llamazares*
Affiliation:
Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
Eduardo J. Molinos
Affiliation:
Institut für Mess - und Regelungstechnik, Karlsruhe Institut fur Technologie, Karlsruhe, Germany Email: eduardo.molinos@kit.edu
Manuel Ocaña
Affiliation:
Department of Electronics, University of Alcalá, Alcalá de Henares, Spain Email: mocana@depeca.uah.es
*
*Corresponding author. E-mail: angel.llamazares@uah.es
Get access Rights & Permissions[Opens in a new window]

Summary

Working with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.

Keywords

DATMOGrid-basedModel-freeModel-based
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 761 - 774
Copyright
© Cambridge University Press 2019

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.)

References

Vu, T.-D. and Aycard., O. “Laser-based Detection and Tracking Moving Objects Using Data-driven Markov Chain Monte Carlo,” In:ICRA (IEEE, 2009) pp. 3800–3806.Google Scholar
Nashashibi, F. and Bargeton., A. “Laser-based Vehicles Tracking and Classification Using Occlusion Reasoning and Confidence Estimation,” In:2008 IEEE Intelligent Vehicles Symposium (June 2008) pp. 847–852.CrossRefGoogle Scholar
Reid., D. “An algorithm for tracking multiple targets,” IEEE Transactions on Automatic Control 24(6), 843–854 (1979).CrossRefGoogle Scholar
Sittler., R. W. “An optimal data association problem in surveillance theory,” IEEE Transactions on Military Electronics 8(2), 125–139 (1964).CrossRefGoogle Scholar
Vo, B.-N. and Ma., W.-K. “The Gaussian mixture probability hypothesis density filter,” IEEE Transactions on Signal Processing 54(11), 4091–4104 (2006).CrossRefGoogle Scholar
Vo, B.-N., Mallick, M., Bar-Shalom, Y., Coraluppi, S., Osborne III, R., Mahler, R. and Vo, B.-T., Multitarget Tracking (American Cancer Society, 2015) pp. 1–15.CrossRefGoogle Scholar
Blackman, S. S., Dempster, R. J., Busch, M. T. and Popoli, R. F., “Imm/mht solution to radar benchmark tracking problem,” IEEE Transactions on Aerospace and Electronic Systems 35(2), 730–738 (1999).CrossRefGoogle Scholar
Cox, I. J. and Hingorani, S. L., “An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence 18(2), 138–150 (1996).CrossRefGoogle Scholar
Fortmann, T., Bar-Shalom, Y. and Scheffe, M., “Sonar tracking of multiple targets using joint probabilistic data association,” IEEE Journal of Oceanic Engineering 8(3), 173–184 (1983).CrossRefGoogle Scholar
Cox, I. J., “A review of statistical data association techniques for motion correspondence,” International Journal of Computer Vision 10(1), 53–66 (1993).CrossRefGoogle Scholar
Kalman, R., “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering (ASME) 82(1), 35–45 (1960).CrossRefGoogle Scholar
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 (Springer, Berlin, Heidelberg, 2009) pp. 91–123.Google Scholar
Chen, X., Wang, X. and Xuan, J., Tracking multiple moving objects using unscented Kalman filtering techniques. CoRR, abs/1802.01235 (2018).Google Scholar
Doucet, A., Godsill, S. and Andrieu, C., “On sequential Monte Carlo sampling methods for Bayesian filtering,” Statistics and Computing 10(3), 197–208 (2000).CrossRefGoogle Scholar
Blom, H. A. P. and Bar-Shalom, Y., “The interacting multiple model algorithm for systems with Markovian switching coefficients,” IEEE Transactions on Automatic Control 33(8), 780–783 (1988).CrossRefGoogle Scholar
Andrieu, C., N. de Freitas, A. Doucet and M. I. Jordan, “An introduction to MCMC for machine learning,” Machine Learning 50(1), 5–43 (2003).CrossRefGoogle Scholar
Arras, K. O., Grzonka, S., Luber, M. and Burgard, W., “Efficient People Tracking in Laser Range Data Using a Multi-hypothesis Leg-tracker with Adaptive Occlusion Probabilities,” In:ICRA (IEEE, 2008) pp. 1710–1715.Google Scholar
Schulz, D., Burgard, W., Fox, D. and B., A. Cremers, “Tracking Multiple Moving Targets with a Mobile Robot Using Particle Filters and Statistical Data Association,” In:Proceedings of the 2001 IEEE International Conference on Robotics and Automation, ICRA 2001, Seoul, Korea (2001) pp. 1665–1670.Google Scholar
Topp, E. A. and Christensen, H. I., “Tracking for Following and Passing Persons,” In:Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’05) , Edmonton, Alberta, Canada (2005) pp. 70–76.Google Scholar
Wang, C.-C., Duggins, D., Gowdy, J., Kozar, J., MacLachlan, R., Mertz, C., Suppe, A. and Thorpe, C., Navlab Slammot Datasets (Carnegie Mellon University, 2004).Google Scholar
Zhao, L. and Thorpe, C., “Qualitative and Quantitative Car Tracking from a Range Image Sequence,” In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 1998) pp. 496–501.Google Scholar
Granström, K., Extended target tracking using PHD filters, PhD thesis, Linköping University (2012).Google Scholar
Chavez-Garcia, R. O. and Aycard, O., “Multiple sensor fusion and classification for moving object detection and tracking,” IEEE Transactions on Intelligent Transportation Systems 17(2), 252–534 (2015).Google Scholar
Friedman, J., Hastie, T. and Tibshirani, R., “Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors),” Ann. Statist. 28(2), 337–407 (2000).CrossRefGoogle Scholar
Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., Koch, O., Kuwata, Y., Moore, D., Olson, E., Peters, S., Teo, J., Truax, R., Walter, M., Barrett, D., Epstein, A., Maheloni, K., Moyer, K., Jones, T., Buckley, R., Antone, M., Galejs, R., Krishnamurthy, S. and Williams, J., “A perception-driven autonomous urban vehicle,” Journal of Field Robotics 25(10), 727–774 (2008).CrossRefGoogle Scholar
Mertz, C., Navarro-Serment, L. E., Duggins, D., Gowdy, J., MacLachlan, R., Rybski, P., Steinfeld, A., Suppe, A., Urmson, C., Vandapel, N., Hebert, M. and Thorpe, C., “Moving object detection with laser scanners,” Journal of Field Robotics 30(1), 17–43 (2013).CrossRefGoogle Scholar
Wang, C.-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,” In:Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Taipei, Taiwan (2003).Google Scholar
Montesano, L., Minguez, J. and Montano, L., “Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation,” In:IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain (2005).Google Scholar
Miyasaka, T., Ohama, Y. and Ninomiya, Y., “Ego-motion Estimation and Moving Object Tracking using Multi-layer LIDAR,” In:Proceedings of the IEEE Intelligent Vehicles Symposium (2009) pp. 151–156.Google Scholar
Vu, T.-D., Burlet, J. and Aycard, O., “Grid-based localization and local mapping with moving object detection and tracking,” Inf. Fusion 12(1), 58–69 (2011).CrossRefGoogle Scholar
Elfes, A., “Using occupancy grids for mobile robot perception and navigation,” Computer 22(6), 46–57 (1989).CrossRefGoogle Scholar
Biswas, R., Limketkai, B., Sanner, S. and Thrun, S., “Towards Object Mapping in Non-stationary Environments with Mobile Robots,” In:2002 IEEE/RSJ International Conference (2002) pp. 1014–1019.Google Scholar
Yang, S.-W. and Wang, C.-C., “Simultaneous egomotion estimation, segmentation, and moving object detection,” J. Field Robotics 28(4), 565–588 (2011).CrossRefGoogle Scholar
Hähnel, D., Triebel, R., Burgard, W. and Thrun, S., “Map Building with Mobile Robots in Dynamic Environments,” In:IEEE International Conference on Robotics and Automation (ICRA) (IEEE Computer Society Press, 2003).Google Scholar
Hähnel, D., Schulz, D. and Burgard, W., “Mobile robot mapping in populated environments,” Advanced Robotics 17(7), 579–597 (2003).CrossRefGoogle Scholar
Tipaldi, G. D. and Ramos, F., “Motion Clustering and Estimation with Conditional Random Fields,” In:2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (October 2009) pp. 872–877.CrossRefGoogle Scholar
van de Ven, J., Ramos, F. and Tipaldi, G. D., “An Integrated Probabilistic Model for Scan-matching, Moving Object Detection and Motion Estimation,” In:ICRA(IEEE, 2010) pp. 887–894.Google Scholar
Wang, D. Z., Posner, I. and Newman, P., “Model-free detection and tracking of dynamic objects with 2D LIDAR,” Int. J. Rob. Res. 34(7), 1039–1063 (2015).CrossRefGoogle Scholar
Coué, C., Pradalier, C., Laugier, C., Fraichard, T. and Bessiere, P., “Bayesian occupancy filtering for multitarget tracking: an automotive Application,” Int. Journal of Robotics Research 25(1), 19–30 (2006). voir basilic: http://emotion.inrialpes.fr/bibemotion/2006/CPLFB06/.CrossRefGoogle Scholar
Dempster, A. P., “Upper and lower probabilities induced by a multivalued mapping,” Ann. Math. Statist. 38(2), 325–339 (1967).CrossRefGoogle Scholar
Kurdej, M., Moras, J., Cherfaoui, V. and Bonnifait, P., Map-Aided Fusion Using Evidential Grids for Mobile Perception in Urban Environment (Springer, Berlin, Heidelberg, 2012) pp. 343–350.Google Scholar
Moras, J., Cherfaoui, V. and Bonnifait, P., “Evidential Grids Information Management in Dynamic Environments,” In:17th International Conference on Information Fusion (FUSION) (July 2014) pp. 1–7.Google Scholar
Saval-Calvo, M., Medina-Valds, L., Castillo-Secilla, J. M., Cuenca-Asensi, S., Martnez, A. and Villagr, J., “A review of the Bayesian occupancy filter,” Sensors 17(2) (2017).CrossRefGoogle Scholar
Fulgenzi, C., Autonomous navigation in dynamic uncertain environment using probabilistic models of perception and collision risk prediction. Thesis, Institut National Polytechnique de Grenoble - INPG (June 2009).Google Scholar
Tay, M. K., Mekhnacha, K., Yguel, M., Coué, C., Pradalier, C., Laugier, C. and Fraichard, T., The Bayesian Occupation Filter (Springer, Berlin, Heidelberg, 2008) pp. 77–98.Google Scholar
Tay, M. K., Mekhnacha, K., Chen, C., Yguel, M. and Laugier, C., “An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments,” International Journal of Vehicle Autonomous Systems 6(1-2), 155–171 (2008).CrossRefGoogle Scholar
Nègre, A., Rummelhard, L. and Laugier, C., “Hybrid Sampling Bayesian Occupancy Filter,” In:2014 IEEE Intelligent Vehicles Symposium Proceedings (2014) pp. 1307–1312.Google Scholar
Adarve, J. D., Perrollaz, M., Makris, A. and Laugier, C., “Computing Occupancy Grids from Multiple Sensors using Linear Opinion Pools,” IEEE International Conference on Robotics and Automation, St Paul, Minnesota, USA (2012).CrossRefGoogle Scholar
Baig, Q., Perrollaz, M. and Laugier, C., “A robust motion detection technique for dynamic environment monitoring: A framework for grid-based monitoring of the dynamic environment,” IEEE Robotics Automation Magazine 21(1), 40–48 (2014).CrossRefGoogle Scholar
Chen, C., Tay, C., Laugier, C. and Mekhnacha, K., “Dynamic Environment Modeling with Gridmap: A Multiple-object Tracking Application,” In:2006 9th International Conference on Control, Automation, Robotics and Vision (2006) pp. 1–6.Google Scholar
Yguel, M., Tay, C., Mekhnacha, K. and Laugier, C., Velocity Estimation on the Bayesian Occupancy Filter for Multi-Target Tracking, Research Report RR-5836, INRIA (2006).Google Scholar
Gindele, T., Brechtel, S., Schroder, J. and Dillmann, R., “Bayesian Occupancy Grid Filter for Dynamic Environments Using Prior Map Knowledge,” In:2009 IEEE Intelligent Vehicles Symposium (2009) pp. 669–676.Google Scholar
Brechtel, S., Gindele, T. and Dillmann, R., “Recursive Importance Sampling for Efficient Grid-based Occupancy Filtering in Dynamic Environments,” In:2010 IEEE International Conference on Robotics and Automation (2010) pp. 3932–3938.Google Scholar
Danescu, R., Oniga, F. and Nedevschi, S., “Modeling and tracking the driving environment with a particle-based occupancy grid,” IEEE Transactions on Intelligent Transportation Systems 12(4), 1331–1342 (2011).CrossRefGoogle Scholar
Nuss, D., Wilking, B., Wiest, J., Deusch, H., Reuter, S. and Dietmayer, K., “Decision-free True Positive Estimation with Grid Maps for Multi-object Tracking,” In:16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) (2013) pp. 28–34.Google Scholar
Mekhnacha, K., Mao, Y., Raulo, D. and Laugier, C., “Bayesian Occupancy Filter Based ‘Fast Clustering-Tracking’ Algorithm,” In:IROS 2008, Nice, France (2008).Google Scholar
Yuan, T., Nuss, D. S., Krehl, G., Maile, M. and Gern, A., “Fundamental Properties of Dynamic Occupancy Grid Systems for Vehicle Environment Perception,” In:2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2015) pp. 153–156.Google Scholar
12
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Detection and Tracking of Moving Obstacles (DATMO): A Review
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Detection and Tracking of Moving Obstacles (DATMO): A Review
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Detection and Tracking of Moving Obstacles (DATMO): A Review
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *