Tracking of Moving Objects from a Moving Vehicle Using a Scanning Laser Rangefinder

MacLachlan, Robert A.; Mertz, Christoph

doi:10.1109/itsc.2006.1706758

Cited by 69 publications

(42 citation statements)

References 5 publications

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“…Specifically, we focus on Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters -both based on RFS models -as they deal with the target-measurement association implicitly. Prior approaches using explicit association [3], [4] are faced with the issues of clutter, where multiple measurements for a single target appear, as well as false associations. PHD filters are furthermore able to robustly and accurately track targets without having prior knowledge over the number of targets, unlike the Joint Probabilistic Data Association (JPDA) filter [4].…”

Section: Related Workmentioning

confidence: 99%

Cooperative multiple dynamic object tracking on moving vehicles based on Sequential Monte Carlo Probability Hypothesis Density filter

Gan

Vasić

Martinoli

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-This paper proposes a generalized method for tracking of multiple objects from moving, cooperative vehicles -bringing together an Unscented Kalman Filter for vehicle localization and extending a Sequential Monte Carlo Probability Hypothesis Density filter with a novel cooperative fusion algorithm for tracking. The latter ensures that the fusion of information from cooperating vehicles is not limited to a fully overlapping Field Of View (FOV), as usually assumed in popular distributed fusion literature, but also allows for a perceptual extension corresponding to the union of the vehicles' FOV. Our method hence allows for an overall extended perception range for all cooperative vehicles involved, while preserving same or improving the accuracy in the overlapping FOV. This method also successfully mitigates noisy sensor measurement and clutter, as well as localization inaccuracies of tracking vehicles using Global Navigation Satellite Systems (GNSS). Finally, we extensively evaluate our method using a high-fidelity simulator for vehicles of varying speed and trajectories.

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Section: Related Workmentioning

confidence: 99%

Cooperative multiple dynamic object tracking on moving vehicles based on Sequential Monte Carlo Probability Hypothesis Density filter

Gan

Vasić

Martinoli

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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“…The planar scanning LASER sensors return information about the (2D) shape and orientation of a vehicle in the near range [8]. The shape is computed from the intersection of the plane of the beam with the object.…”

Section: Multisensor Setupmentioning

confidence: 99%

“…Fig. 3 (right) shows edge targets which are extracted from the raw data of the scanning lasers (a heuristic for the planar lasers is described in [8]). Edge target features are "L" shaped features, which describe objects which either have two edges with a near 90 angle or objects where only one edge is visible.…”

Section: B Movement Classificationmentioning

confidence: 99%

Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments

Darms

Rybski

Urmson

2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan Racing's autonomous vehicle for the Urban Grand Challenge. Results are presented and discussed.

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“…People tracking typically requires carefully engineered or learned features for track identification and data association and often a-priori information about motion models. This has been shown to be the case also for geometrically simpler and rigid object such as vehicles in traffic scenarios [11]. Cui et al [12] describe a system for tracking single persons within a larger set of persons, given the relevant motion models are known.…”

Section: Related Workmentioning

confidence: 99%

Classifying Dynamic Objects: An Unsupervised Learning Approach

Luber

Arras

Plagemann

et al. 2008

Robotics: Science and Systems IV

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Abstract-For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given training sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information.

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Tracking of Moving Objects from a Moving Vehicle Using a Scanning Laser Rangefinder

Cited by 69 publications

References 5 publications

Cooperative multiple dynamic object tracking on moving vehicles based on Sequential Monte Carlo Probability Hypothesis Density filter

Cooperative multiple dynamic object tracking on moving vehicles based on Sequential Monte Carlo Probability Hypothesis Density filter

Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments

Classifying Dynamic Objects: An Unsupervised Learning Approach

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