A Collaborative Sensor Fusion Algorithm for Multi-object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

Abstract-This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and real vehicles endowed with automotive-grade sensors. We have extended our previously presented cooperative sensor fusion algorithm with a fusion weight optimization method and implemented it on a vehicle that we denote as the ego vehicle. The algorithm fuses information obtained from one or more vehicles located within a certain range (that we call cooperative), which are running a multi-object tracking PHD filter, and which are sharing their object estimates. The algorithm is evaluated on two Citroën C-ZERO prototype vehicles equipped with Mobileye cameras for object tracking and lidar sensors from which the ground truth positions of the tracked objects are extracted. Moreover, the algorithm is evaluated in simulation using simulated C-ZERO vehicles and simulated Mobileye cameras. The ground truth positions of tracked objects are in this case provided by the simulator. Multiple experimental runs are conducted in both simulated and real-world conditions in which a few legacy vehicles were tracked. Results show that the cooperative fusion algorithm allows for extending the sensing field of view, while keeping the tracking accuracy and errors similar to the case in which the vehicles act alone.

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“…The fused set is further used by the tracking component. The two components together form the C-GM-PHD filter [10].…”

Section: Methodsmentioning

confidence: 99%

“…This is a very limiting assumption for the application of the aforementioned methods in the field of (moving) vehicles. In our previous work [10], we proposed a Cooperative GM-PHD (C-GM-PHD) filter, which relaxes this assumption. We validated our cooperative filter in a high-fidelity simulation environment using lidar sensors.…”

Section: Firstnamelastname@epflchmentioning

confidence: 99%

A system implementation and evaluation of a cooperative fusion and tracking algorithm based on a Gaussian Mixture PHD filter

Vasić

Mansolino

Martinoli

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Vasic et al [5], for example, extended the GM-PHD filter introduced in [6] to involve cooperation and fusion of information between two agents each running an instance of the GM-PHD filter.…”

Section: Related Workmentioning

confidence: 99%

“…While the case for the feasibility of a collaborative GM-PHD filter (C-GM-PHD) in tracking multiple targets was presented in [5], the C-GM-PHD approach has limitations in the fact that single-target transitional densities and likelihood functions must be Gaussian [7]. Transitional densities also have to be linear or approximately linear in order for the GM-PHD and approaches based on it to work.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, their single state transition model is also defined by (20) - (23). As such, we also assume that the measurement obtained contains the positional information as well as orientation of tracked targets by preprocessing data from the laser range sensor, as described in [5]. The likelihood model is given as a multivariate Gaussian distribution:…”

Section: Ukf For Localization Of Tracking Vehiclementioning

confidence: 99%

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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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A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter

2019

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A Collaborative Sensor Fusion Algorithm for Multi-object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

Cited by 23 publications

References 23 publications

A system implementation and evaluation of a cooperative fusion and tracking algorithm based on a Gaussian Mixture PHD filter

A system implementation and evaluation of a cooperative fusion and tracking algorithm based on a Gaussian Mixture PHD filter

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

A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter

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