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Detection, classification and tracking of moving objects in a 3D environment
Abstract
In this paper, we present a framework based on 3D range data to solve the problem of simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic environments. The basic idea is to use an octree based Occupancy Grid representation to model dynamic environment surrounding the vehicle and to detect moving objects based on inconsistencies between scans. The proposed method for the discrimination between moving and stationary objects without a priori knowledge of the targets is the main contribution of this paper. Moreover, the detected moving objects are classified and tracked using Global Nearest Neighbor (GNN) technique. The proposed method can be used in conjunction with any type of range sensors however we have demonstrated it using the data acquired from a Velodyne HDL-64E LIDAR sensor. The merit of our approach is that it allows for an efficient three dimensional representation of a dynamic environment, keeping in view the enormous amount of information provided by 3D range sensors.
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“Image-based methods, however, need to establish some appearance model, which may consider the use of color histograms, gradients and other features such as KLT to evaluate the similarity [185].”
From A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2020) · cites this paper as [185]
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No references could be resolved yet.
Research works printed but not linked: 16
- wurm, 0, OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems, Proceedings of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation Anchorage AK USA May 2010
- 10.1007/978-3-540-75404-6_3 · DOI
- wang, 2004, Simultaneous localization mapping and moving object tracking
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- 10.1016/j.inffus.2010.01.004 · DOI
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- 10.1109/ROBOT.2006.1641929 · DOI
- 10.1109/2.30724 · DOI
- nu?chter, 2007, 6D SLAM-3D mapping outdoor environments: Research articles, Journal of Field Robotics, 24, 699, 10.1002/rob.20209 · DOI
- moravec, 1996, Robot spatial perception by stereoscopic vision and 3d evidence grids
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Cited by
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“Object tracking is also often referred to as multiple object tracking (MOT) [180] and detection and tracking of multiple objects (DATMO) [181].”
Background
Metadata
- Authors
- Azim, A., & Aycard, O.
- Affiliations
- Laboratoire d'Informatique de Grenoble (FR)
- Venue
- 2012 IEEE Intelligent Vehicles Symposium
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 802-807
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W1994114209
- Cited by
- 171
- References
- 0
- Citations by year
- 2025: 10 · 2024: 9 · 2023: 14 · 2022: 11 · 2021: 19 · 2020: 20 · 2019: 12 · 2018: 15 · 2017: 13 · 2016: 13 · 2015: 15 · 2014: 10 · 2013: 10
- FWCI
- 20.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- moving object detection, moving object tracking, moving object classification, dynamic environments, 3D range data, LiDAR, SLAM, occupancy grid, octree, dynamic environment modeling
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- The paper
- Azim, A., & Aycard, O. (2012). Detection, classification and tracking of moving objects in a 3D environment. 2012 IEEE Intelligent Vehicles Symposium. https://doi.org/10.1109/ivs.2012.6232303
