Definition of the Subject and Its Importance
There have been numerous research efforts toward generating 2D/3D urban models using mobile robots . In addition, research has focused on robot-centric mapping and moving object detection/tracking for online perception and navigation. However, to date, there has been little work on generating a realistic 3D copy of a dynamic environment that describes the state of both static and dynamic objects at the moment. Toward the goal of developing omni-directional range sensing in a dynamic urban scene using an intelligent vehicle, research has mainly focused on the fundamental issues of multi-laser sensor system calibration and scene understanding in contextual map generation. Here, both system and algorithmic development are presented as well as experimental research demonstrating that a geometric and contextual representation of static objects such as buildings, trees, and roads, as well as the motion of dynamic...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- Classification:
-
Annotating a data segment or an object by a class label.
- Contextual map:
-
A map containing high-level knowledge beyond geometry, such as object, class, and motion.
- Data alignment:
-
Integrating the instantaneous measurements from sensors’ coordinate system(s) to a global coordinate system.
- Dynamic mapping:
-
A mapping technology that uses a dynamic procedure or generates a map of a dynamic environment.
- Multi-laser sensing system:
-
A sensor system that makes collaborative use of a number of laser range scanners.
- Range image:
-
A 2D image where the value of each pixel is a range distance, with the pixel index corresponding to range angle and scanning sequence, so that a 3D coordinate can be retrieved for each pixel of the range image.
- Scene understanding:
-
Converting from low-level knowledge to high-level knowledge of an environment.
- Segmentation:
-
Making partitions on a data set, where in each partition cell (i.e., segment), data has the property of certain homogeneity.
- Sensor calibration:
-
Finding a set of parameters that describes internal or external sensor geometry.
Bibliography
International Society of Photogrammetry and Remote Sensing. http://www.isprs.org/
Collins R, Hanson A, Riseman E (1994) Site model acquisition under the UMass RADIUS project. In: Proceedings of arpa image understanding workshop, Monterey, CA, pp 351–358
Gruen A (1998) TOBAGO – a semi-automated approach for the generation of 3-D building models. ISPRS J Photogramm Remote Sens 53(2):108–118
Forstner W (1999) 3D-city models: automatic and semiautomatic acquisition methods. In: Proceedings photogrammetric week, University of Stuttgart, Institute for photogrammetry, pp 291–303
Shiode N (2001) 3D urban model: recent developments in the digital modeling of urban environments in three-dimensions. GeoJournal 52(3):263–269
Ellum C, El-Sheimy N (2000) The development of a backpack mobile mapping system. Int Arch Photogramm Remote Sensing XXXII(B2):184–191, Amsterdam
He G, Orvets G (2000) Capturing road network data using mobile mapping technology. Int Arch Photogramm Remote Sensing XXXIII(B2):272–277, Amsterdam
Silva JFC, Camargo PO, Oliveira RA (2000) A street map built by a mobile mapping system. Int Arch Photogramm Remote Sensing XXXIII(B2):510–517, Amsterdam
El-Sheimy N (2000) Mobile multi-sensor systems: the new trend in mapping and GIS applications. IAG J Geodesy, vol. 121, Geodesy beyond 2000: the challenges of the first decade. Springer, Berlin/Heidelberg, pp 319–324
Li R (1997) Mobile mapping: an emerging technology for spatial data acquisition. Photogramm Eng Remote Sens 63(9):1085–1092
Früh C, Zakhor A (2004) An automated method for large-scale, ground-based city model acquisition. Int J Comput Vis 60(1):5–24
Ikeuchi K, Sakauchi M, Kawasaki H, Sato I (2004) Constructing virtual cities by using panoramic images. Int J Comput Vis 53(3):237–247
Zhao H, Shibasaki R (2003) Special issue on computer vision system: reconstructing textured CAD model of urban environment using vehicle-borne laser range scanners and line cameras. Mach Vis Appl 14(1):35–41
Google Earth (2004) http://earth.google.com
Microsoft Virtual Earth (2006) http://www.microsoft.com/virtualearth
Google StreetView (2007) http://maps.google.com/help/maps/streetview
StreetMapper (2007) http://www.streetmapper.net
City Grid (2006) http://www.cybercity.tv
Cyber City (2007) http://www.cybercity.tv
Hu J, You S, Neumann U (2003) Approaches to large-scale urban modeling. IEEE Comput Graph Appl 23(6):62–69
Thrun S (2002) Robotic mapping: a survey. CMU-CS-02-11
DARPA (2004) DARPA grand challenge rulebook. http://www.darpa.mil/grandchallenge05/Rules_8oct04.pdf
DARPA (2006) DARPA grand challenge rulebook. http://www.darpa.mil/grandchallenge/docs/Urban_Challenge_Rules_121106.pdf
Journal of Field Robotics: Special issue on the 2007 DARPA urban challenge, Part I 25(8)
Journal of Field Robotics: Special issue on the 2007 DARPA urban challenge, Part II 25(9)
Nuchter A, Lingemann K, Hertzberg J, Surmann H (2007) 6D SLAM – 3D mapping outdoor environments. J Field Robot 24(8/9):699–722
Zhao H, Shibasaki R (2003) A vehicle-borne urban 3D acquisition system using single-row laser range scanners. IEEE Trans SMC Part B: Cybern 33–4:658–666
Allen P, Atamos I, Gueorguiev A, Gold E, Blaer P (2001) AVENUE: automated site modeling in urban environments. In: Proceedings of the 3rd international conference on 3D digital imaging and modeling, Quebec City, pp 357–364
Georgiev A, Allen PK (2004) Localization methods for a mobile robot in urban environments. IEEE Trans Robot Automat (TRO) 20(5):851–864
Hahnel D, Burgard W, Fox D, Thrun S (2003) An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, Las Vegas, pp 206–211
Wang CC (2004) Simultaneous localization, mapping and moving object tracking. PhD dissertation, Carnegie Mellon University, CMU-RI-TR-04-23
Vu T, Aycard O, Appenrodt N (2007) Online localization and mapping with moving object tracking in dynamic outdoor environment. In: Proceedings IEEE intelligent vehicle symposium, Istanbul, pp 190–195
Weiss T, Schiele B, Dietmayer K (2007) Robust driving path detection in urban and highway scenarios using a laser scanner and online occupancy grids. In: Proceedings of the IEEE intelligent vehicle symposium, Istanbul, pp 184–189
Zhao H, Chiba M, Shibasaki R, Shao X, Cui J, Zha H (2008) SLAM in a dynamic large outdoor environment using a laser scanner. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), Pasadena, CA, pp 1455–1462
Velodyne HDL-64E (2007) http://www.velodyne.com/lidar/products/overview.aspx
SICK LMS2** (2001) http://www.sick.com
Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: Part I. IEEE Rob Autom Mag 13(2):99–108
Bailey T, Durrant-Whyte H (2006) Simultaneous localization and mapping: Part II. IEEE Rob Autom Mag 13(3):108–117
Streller D, Dietmayer K (2004) Object tracking and classification using a multiple hypothesis approach. In: Proceedings of the IEEE intelligent vehicles symposium, Parma, pp 808–812
Fayad F, Cherfaoui V (2007) Tracking objects using a laser scanner in driving situation based on modeling target shape. In: Proceedings of the IEEE intelligent vehicle symposium, Istanbul, pp 44–49
Zhao H, Liu Y, Zhu X, Zhao Y, Zha H (2010) Scene understanding in a large dynamic environment through a laser-based sensing.In: IEEE international conference on robotics and automation, Anchorage, pp 127–133
Zhao H, Xiong L, Jiao Z, Cui J, Zha H (2009) Sensor alignment towards an omni-directional measurement using an intelligent vehicle. In: Proceedings of the IEEE intelligent vehicle symposium, Kobe, pp 292–298
Zhao H, Shibasaki R (2001) High accurate positioning and mapping in urban area using laser range scanner. In: Proceedings of IEEE intelligent vehicles symposium, Tokyo, pp 125–132
Zhao H, Chiba M, Shibasaki R, Shao X, Cui J, Zha H (2009) A laser scanner based approach towards driving safety and traffic data collection. IEEE Trans Intell Transport Syst 10(3):534–546
Cheok GS, Leigh S, Rukhin A (2002) Calibration experiments of a laser scanners. NISTIR 6922:121
Santala J, Joala V (2003) On the calibration of a ground-based laser scanner. TS12.4, FIG working week
Mahlisch M, Hering R, Ritter W, Dietmayer K (2006) Heterogeneous fusion of video, Lidar, ESP data for automotive ACC vehicle tracking. In: Proceedings of IEEE international conference on multisensor fusion and integration for intelligent systems, Heidelberg, pp 139–144
Rodriguez F, Sergio A, Fremont V, Bonnifait P (2008) Extrinsic calibration between a multi-layer LIDAR and a camera. In: Proceedings of IEEE international conference on multisensor fusion and integration for intelligent systems, Seoul, pp 214–219
Zhao H, Chen Y, Shibasaki R (2007) An efficient extrinsic calibration of a multiple laser scanners and cameras’ sensor system on a mobile platform. In: IEEE intelligent vehicles symposium, Istanbul, pp 422–427
Gao C, Spletzer JR (2010) On-line calibration of multiple LIDARs on a mobile vehicle platform. In: Proceedings of IEEE international conference on robotics and automation, Anchorage, pp 279–284
Besl PJ, McKay ND (1992) A method for registration of 3-D shape. IEEE Trans Pattern Anal Mach Intell 14:239–256
Chen Y, Medion G (1992) Object modeling by registration of multiple range images. Image Vis Comput 10(3):145–155
Zhang Z (1994) Iterative point matching for registration of a free-from curves and surfaces. Int J Comput Vis 13:119–152
Hahnel D, Burgard W, Thrun S (2003) Learning compact 3d models of indoor and outdoor environments with a mobile robot. Rob Autom Syst 44:15–27
Althaus P, Christensen H (2003) Behavior coordination in structured environments. Adv Robot 17(7):657–674
Mendes A, Nunes U (2004) Situation-based multi-target detection and tracking with laserscanner in outdoor semi-structured environment. In: Proceedings IEEE/RSJ international conference on intelligent robots and systems, Sendai
Posner I, Cummins M, Newman P (2008) Online generation of scene descriptions in urban environments. Rob Autom Syst 56(11):901–914
Douillard B (2009) Vision and laser based classification in urban environments. PhD thesis, University of Sydney
Nuchter A, Lingemann K, Hertzberg J, Surmann H (2009) 6D SLAM – 3D mapping outdoor environments. J Field Robot 24(8–9):699–722
Martinez-Mozos O, Stachniss C, Burgard W (2005) Supervised learning of places from range data using AdaBoost. In: IEEE international conference on robotics and automation (ICRA), Barcelona, pp 1742–1747
Triebel R, Kersting K, Burgard W (2006) Robust 3D scan point classification using associative Markov networks. In: IEEE international conference on robotics and automation (ICRA), Orlando, FL
Douillard B, Fox D, Ramos FT (2008) Laser and vision based outdoor object mapping. In: Proceedings of robotics: science and systems, Zurich
Posner I, Cummins M, Newman P (2009) A generative framework for fast urban labeling using spatial and temporal context. Auton Robot 26(2–3):153–170
Hoover A, Jean-Baptiste G, Jiang X, Flynn PJ, Bunke H, Goldgof D, Bowyer K (1996) A comparison of range image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 18(7):673–689
Katsoulas D, Bastidas CC, Kosmopoulos D (2008) Superquadic segmentation in range image via fusion of region and boundary information. IEEE Trans Pattern Anal Mach Intell 20(5):781–795, 2008
Weingarten J, Siegwart R (2005) EKF-based 3D SLAM for structured environment. In: IEEE/RSJ international conference on intelligent robots and systems, Edmonton, Alberta, pp 2089–2094
Han F, Tu Z, Zhu SC (2004) Range image segmentation by an effective jump-diffusion method. IEEE Trans Pattern Anal Mach Intell 26(9):1138–1153
Borenstein E, Ullman S (2008) Combined top-down/bottom-up segmentation. IEEE Trans Pattern Anal Mach Intell 30(12):2109–2125
Malisiewicz T, Efros AA (2008) Recognition by association via learning per-exemplar distances. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Anchorage
Porway J, Wange K, Zhu SC (2008) A hierarchical and contextual model for aerial image understanding. In: Proceedings of the IEEE international conference on computer vision and pattern recognition (CVPR), Anchorage, pp 1–8
Tu Z, Chen X, Yuille A, Zhu SC (2005) Image parsing: unifying segmentation, detection and recognition. Int J Comput Vis 63(2):113–140
Golovinskiy A, Kim VG, Funkhouser T (2009) Shape-based recognition of 3D point clouds in urban environments. In: IEEE international conference on computer vision, Kyoto, pp 2154–2161
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905
Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59:167–181
Jiang X, Bunke H (1994) Fast segmentation of range images into planar regions by scan line grouping. Mach Vis Appl 7:115–122
Rosin PL, West GAW (1995) Nonparametric segmentation of curves into various representations. IEEE Trans Pattern Anal Mach Intell 17:1140–1153. http://users.cs.cf.ac.uk/Paul.Rosin
Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/∼cjlin/libsvm
PKU Omni Smart Sensing – POSS (2010) http://www.poss.pku.edu.cn
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this entry
Cite this entry
Zhao, H., Xiong, L., Liu, Y., Zhu, X., Zhao, Y., Zha, H. (2013). Dynamic Environment Sensing Using an Intelligent Vehicle . In: Ehsani, M., Wang, FY., Brosch, G.L. (eds) Transportation Technologies for Sustainability. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5844-9_482
Download citation
DOI: https://doi.org/10.1007/978-1-4614-5844-9_482
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-5843-2
Online ISBN: 978-1-4614-5844-9
eBook Packages: EnergyReference Module Computer Science and Engineering