Rao-Blackwellised PHD SLAM

Mullane, John; Vo, Ba-Ngu; Adams, Martin

doi:10.1109/robot.2010.5509626

Cited by 28 publications

(19 citation statements)

References 22 publications

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“…The method in which this is performed is the focus of this paper, and we will explore this in greater details in section III. 5) Merging and Pruning of the Map: Gaussians with small weights are eliminated from the intensity function, while Gaussians that are close to each other are merged together [2,13]. This approximation is critical in limiting the computational requirement of the RB-PHD filter.…”

Section: B the Rb-phd Filtermentioning

confidence: 99%

An improved weighting strategy for Rao-Blackwellized Probability Hypothesis Density simultaneous localization and mapping

Leung

Inostroza

Adams

2013

2013 International Conference on Control, Automation and Information Sciences (ICCAIS)

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The use of random finite sets (RFSs) in simultaneous localization and mapping (SLAM) for mobile robots is a new concept that provides several advantages over traditional vector-based approaches. These include: 1) the incorporation of detection statistics, as well as the usual spatial uncertainty, in an estimation algorithm, 2) the ability to estimate the number of landmarks in a map, and 3) the circumvention of the need for data association heuristics. Solutions to SLAM can be obtained through the Rao-Blackwellized Probability Hypothesis Density (RB-PHD) filter, which is an approximation of the Bayes filter for RFSs using both particles to represent the robot trajectories, and Gaussian mixtures to represent their associated maps. This paper proposes an improved multi-feature particle weighting strategy for the RB-PHD filter and shows through simulations that it outperforms existing weighting strategies. The proposed strategy makes the RB-PHD filter a generalization of multi-hypothesis (MH) FastSLAM, a vector-based SLAM solution that uses the RB-particle filter.

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Section: B the Rb-phd Filtermentioning

confidence: 99%

An improved weighting strategy for Rao-Blackwellized Probability Hypothesis Density simultaneous localization and mapping

Leung

Inostroza

Adams

2013

2013 International Conference on Control, Automation and Information Sciences (ICCAIS)

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“…The smoothed features are then clustered based on nearby pixels and pruned according to a minimum and maximum area constraints. seminal developments in the tracking community [16], [17], recent SLAM investigations suggest that a feature map is more appropriately represented as a set of features, requiring the tools of random finite set (RFS) theory [9], [18], [19]. This approach is also adopted in this paper.…”

Section: B Clustering and Feature Extractionmentioning

confidence: 99%

“…where a Rao-Blackwellised implementation implies the mapping recursion is approximated by a Gaussian Mixture PHD Filter, and the trajectory recursion by a Particle Filter [18]. The calculation of the particle weighting likelihood however, requires the evaluation of,…”

Section: The Filtermentioning

confidence: 99%

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X-band radar based SLAM in Singapore's off-shore environment

Mullane

Keller

Rao

et al. 2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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This paper presents a simultaneous localisation and mapping (SLAM) algorithm implemented on an autonomous sea kayak with a commercial off-the-shelf X-band marine radar mounted. The Autonomous Surface Craft (ASC) was driven in an off-shore test site in Singapore's southern Selat Puah marine environment. Data from the radar, GPS and an inexpensive singleaxis gyro data were logged by an on-board processing unit as the ASC traversed the environment, which comprised geographical and surface vessel landmarks. An automated feature extraction routine is presented, based on a probabilistic landmark detector, followed by a clustering and centroid approximation approach. With restrictive feature modeling, and a lack of vehicle control input information, it is demonstrated that via the novel RB-PHD-SLAM Filter, useful results can be obtained, despite an actively rolling and pitching ASC on the sea surface. In addition, the merits of investigating ASC SLAM are demonstrated, particularly with respect to the map estimation, obstacle avoidance and target tracking problems. Despite the presence of GPS and gyro data, heading information on such small ASC's is greatly compromised which induces large sensing error, further accentuate by the large range of the radar sensor. This work is a step towards realising an ASC capable of performing environmental or security surveillance and reporting a real-time active awareness of the above-water scene.

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“…A Gaussian mixture implementation for the CPHD has also been proposed [11]. Recently, Mullane, Vo and Adams proposed a SLAM algorithm which combines a Rao-Blackwellized particle filter with the Gaussian mixture PHD filter for estimation of map landmarks [12]- [14]. In comparison to more established SLAM methods, it is most similar to the FastSLAM family of algorithms [15], but performs much better when faced with increased levels of measurement clutter.…”

mentioning

confidence: 99%

SLAM with single cluster PHD filters

Lee

Clark

Salví

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Recent work by Mullane, Vo, and Adams has re-examined the probabilistic foundations of feature-based Simultaneous Localization and Mapping (SLAM), casting the problem in terms of filtering with random finite sets. Algorithms were developed based on Probability Hypothesis Density (PHD) filtering techniques that provided superior performance to leading feature-based SLAM algorithms in challenging mea surement scenarios with high false alarm rates, high missed detection rates, and high levels of measurement noise. We investigate this approach further by considering a hierarchical point process, or single-cluster multi-object, model, where we consider the state to consist of a map of landmarks conditioned on a vehicle state. Using Finite Set Statistics, we are able to find tractable formulae to approximate the joint vehicle-landmark state based on a single Poisson multi-object assumption on the predicted density. We describe the single-cluster PHD filter and the practical implementation developed based on a particle-system representation of the vehicle state and a Gaussian mixture approximation of the map for each particle.Synthetic simulation results are presented to compare the novel algorithm against the previous PHD filter SLAM algorithm.Results presented indicate a superior performance in vehicle and map landmark localization, and comparable performance in landmark cardinality estimation.

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Rao-Blackwellised PHD SLAM

Cited by 28 publications

References 22 publications

An improved weighting strategy for Rao-Blackwellized Probability Hypothesis Density simultaneous localization and mapping

An improved weighting strategy for Rao-Blackwellized Probability Hypothesis Density simultaneous localization and mapping

X-band radar based SLAM in Singapore's off-shore environment

SLAM with single cluster PHD filters

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