Divergence detectors for the &amp;#x03B4;-generalized labeled multi-Bernoulli filter

Reuter, Stephan; Vo, Ba-Tuong; Wilking, Benjamin; Meißner, Daniel; Dietmayer, Klaus

doi:10.1109/sdf.2013.6698263

Cited by 6 publications

(5 citation statements)

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“…Popular data association approaches are different variants of local or global nearest neighbor (NN/GNN) [65] association using fixed one-to-one associations, joint probabilistic data association (JPDA) [41] using association probabilities with a weighted average of all validated measurements, or multiple hypothesis tracking (MHT) [17,96] using different hypotheses of the data association and track update. Recently, multi-object tracking approaches based on random finite sets (RFS) are increasingly used, e.g., [15,74,[98][99][100]133], modeling not only the object states as random numbers, but also the number of objects and measurements itself. In contrast to common object-individual tracking approaches with separate filters, this enables a more generic estimation of uncertainties about object instances and the corresponding data association.…”

Section: Challenges Of Multi-sensor Environment Perceptionmentioning

confidence: 99%

Grid-Based Object Tracking

Steyer¹

2021

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Mobile robots require an accurate environment perception to plan intelligent maneuvers and avoid collisions. This thesis presents a novel multi sensor environment estimation strategy that fully combines tracking moving objects and mapping the static environment. The basic idea is to fuse and accumulate measurement data by a dynamic occupancy grid model, whereas moving objects are extracted subsequently based on that generic low-level grid representation. Overall, this work results in a robust and consistent estimation of arbitrary objects and obstacles, which is demonstrated in the context of autonomous driving in complex unstructured environments. Contents Notations VIII Abstract XI 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Challenges of Multi-Sensor Environment Perception . . . . . . . . . . . . . 2 1.3 Main Contribution and Outline of This Work . . . . . . . . . . . . . . . . 8 2 Measurement Grid Representation and Fusion 13 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.2...

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Section: Challenges Of Multi-sensor Environment Perceptionmentioning

confidence: 99%

Grid-Based Object Tracking

Steyer¹

2021

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“…In their publication about PHD-SLAM, Mullane et al already showed that this likelihood can be evaluated for arbitrary choices of map RFS [9], [10], although the actual choice of it has significant performance effects [15] due to the respective approximations. Nevertheless, in contrast to some other approaches, the LMB-SLAM filter offers a direct way to calculate the particle weights, since the update equation of the -GLMB filter already provides an analytic solution for the normalizing constant of the multi-object Bayes filter [16], [7]. The normalizing constant for the updates which follow equation (12) is given by the denominator of the hypotheses weights (13): (16) Thus the weight of particle at time step is given by .…”

Section: Updating the Vehicle's Trajectorymentioning

confidence: 99%

The Labeled Multi-Bernoulli SLAM Filter

Deusch

Reuter

Dietmayer

2015

IEEE Signal Process. Lett.

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In this contribution, a new algorithm addressing the simultaneous localization and mapping (SLAM) problem is proposed: a Rao-Blackwellized implementation of the Labeled Multi-Bernoulli SLAM (LMB-SLAM) filter. Further, we establish that the LMB-SLAM does not require the approximations used in Probability Hypothesis Density SLAM (PHD-SLAM). The LMB-SLAM is shown to outperform PHD-SLAM in simulations by providing a more accurate map as well as an improved estimate of the vehicle's trajectory which is an expected result due to the superior performance of the LMB filter in tracking applications.Index Terms-Labeled multi-bernoulli, localization, mapping, random finite sets, SLAM.1070-9908

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“…Since then, in order to obtain better performance, Vo et al proposed a class of multi-target tracking filters based on the labeled RFS theory and the multi-Bernoulli filter that can generate trajectories while estimating targets' states and are closed under the Chapman-Kolmogorov equation. Those filters are represented by the delta generalized labeled multi-Bernoulli (δ-GLMB) [24][25][26][27] filter and the labeled multi-Bernoulli (LMB) [28,29] filter. As an efficient approximation of the δ-GLMB, the LMB filter's tracking accuracy is very close to that of the δ-GLMB filter.…”

Section: Introductionmentioning

confidence: 99%

“…In the following, the iteration at time k is omitted for convenience. We follow the notations representation of LMB filters in the literature [28].…”

Section: Introduction Of Labeled Multi-bernoulli Filtermentioning

confidence: 99%

“…The static clutter of the three types of clutter is easily removed by the velocity characteristics of the point cloud, and the other two types of clutter, CDC and NCDC, are the main forms of clutter that need to be estimated. The average clutter rate is accumulated from the clutter rates of CDC and NCDC, as described in Equation (28). However, the final clutter rate and the spatial distribution of the clutter together contribute to the computation of the clutter intensity, as described in Equation (27), which in turn leads to a more accurate state update and target number estimation of the filters, as described in Equations ( 10), (20), and (25).…”

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HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

Li,

Wang

et al. 2023

Remote Sensing

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The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter.

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Divergence detectors for the δ-generalized labeled multi-Bernoulli filter

Cited by 6 publications

References 10 publications

Grid-Based Object Tracking

Grid-Based Object Tracking

The Labeled Multi-Bernoulli SLAM Filter

HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

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