Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception

Aeberhard, Michael; Schlichtharle, S.; Kaempchen, Nico; Bertram, Torsten

doi:10.1109/tits.2012.2202229

Cited by 85 publications

(41 citation statements)

References 22 publications

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“…In [143], using a pseudo-measurement approach, a joint registration, association and fusion method at distributed architecture was developed. In [144], using information matrix fusion, a track-to-track fusion approach was presented for automotive environment perception. Therefore, many heterogeneous sensor data can be fused for vehicle tracking [145].…”

Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

View full text Add to dashboard Cite

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“…As reported in [17], the fusion architecture using the cross-covariance compensation and the IMF, denoted as TTF_CCC_IMF, demonstrates similar performance to that of the CKF. While the TTF_SCIF_IMF and the TTF_CCC_IMF are both track-totrack fusion architectures that have the potential to achieve centralized architecture comparable performance, the TTF_SCIF_IMF enjoys a further advantage, i.e.…”

Section: Discussionmentioning

confidence: 80%

“…Recently, the authors in [17] propose a track-to-track fusion architecture using the information matrix filter (IMF), which can well handle track temporal correlation. To handle track spatial correlation, this method adopts traditional compensation algorithms [18][19] that require computing the cross-covariance among different sensor tracks.…”

Section: Track-to-track Fusion Using Split Covariance Intersection Fimentioning

confidence: 99%

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Track-to-track fusion using split covariance intersection filter-information matrix filter (SCIF-IMF) for vehicle surrounding environment perception

Nashashibi

Lefaudeux

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Vehicle surrounding environment perception is an important process for many applications. Nowadays, a tendency is to incorporate redundant and complementary sensors into an intelligent vehicle, in order to enhance its perception ability; then an essential issue arises naturally, i.e. what fusion architecture can be used to combine the data from multiple sensors? In this paper, we propose a new track-totrack fusion architecture using the split covariance intersection filter-information matrix filter (SCIF-IMF). The basic idea is to use the IMF (adapted for estimates in split form) to handle the track temporal correlation of each sensor system and to use the SCIF to handle track spatial correlation. The proposed architecture enjoys complete sensor modularity and thus enables flexible self-adjustment. A simulation based comparative study is presented, which shows that the track-totrack fusion architecture using the SCIF-IMF can achieve centralized architecture comparable performance.

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“…With well-controlled communication, in which the packet loss rate is low and there are merely consecutive packets lost, each robot is expected to receive all control data and measurements from the other robots, and the global state is inferred in a centralized manner. In belief-based approaches [9][5] [10] [11] [12][13] [14], each robot firstly fuses its own control data and measurements into a local belief in a distributed way. Then the local beliefs are shared to the teammate robots, and the global state is inferred by merging the local beliefs.…”

Section: Introductionmentioning

confidence: 99%

Communication adaptive multi-robot simultaneous localization and tracking via hybrid measurement and belief sharing

Chang

Wang³

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Existing multi-robot cooperative perception solutions can be mainly classified into two categories, measurement-based and belief-based, according to the information shared among robots. With well-controlled communication, measurement-based approaches are expected to achieve theoretically optimal estimates while belief-based approaches are not because the cross-correlations between beliefs are hard to be perfectly estimated in practice. Nevertheless, belief-based approaches perform relatively stable under unstable communication as a belief contains the information of multiple previous measurements. Motivated by the observation that measurement sharing and belief sharing are respectively superior in different conditions, in this paper a hybrid algorithm, communication adaptive multi-robot simultaneous localization and tracking (ComAd MR-SLAT), is proposed to combine the advantages of both. To tackle the unknown or unstable communication conditions, the information to share is decided by maximizing the expected uncertainty reduction online, based on which the algorithm dynamically alternates between measurementsharing and belief-sharing without information loss or reuse. The proposed ComAd MR-SLAT is evaluated in communication conditions with different packet loss rates and bursty loss lengths. In our experiments, ComAd MR-SLAT outperforms measurement-based and belief-based MR-SLAT in accuracy. The experimental results demonstrate the effectiveness of the proposed hybrid algorithm and exhibit that ComAd MR-SLAT is robust under different communication conditions.

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Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception

Cited by 85 publications

References 22 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Track-to-track fusion using split covariance intersection filter-information matrix filter (SCIF-IMF) for vehicle surrounding environment perception

Communication adaptive multi-robot simultaneous localization and tracking via hybrid measurement and belief sharing

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