L-SLAM: Reduced dimensionality FastSLAM with unknown data association

Zikos, Nikos; Petridis, V.

doi:10.1109/icra.2011.5979921

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…The idea that certain linear components can be factored out as proposed in this paper is related to that in Zikos and Petridis (2011). In that work the authors propose a Rao-Blackwellized particle filtering approach in which first the robot orientations are estimated using a particle filter.…”

Section: Remarkmentioning

confidence: 98%

Dimensionality reduction for point feature SLAM problems with spherical covariance matrices

et al. 2015

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a b s t r a c tThe main contribution of this paper is the dimensionality reduction for multiple-step 2D point feature based Simultaneous Localization and Mapping (SLAM), which is an extension of our previous work on one-step SLAM (Wang et al., 2013). It has been proved that SLAM with multiple robot poses and a number of point feature positions as variables is equivalent to an optimization problem with only the robot orientations as variables, when the associated uncertainties can be described using spherical covariance matrices. This reduces the dimension of original problem from 3m + 2n to m only (where m is the number of poses and n is the number of features). The optimization problem after dimensionality reduction can be solved numerically using the unconstrained optimization algorithms. While dimensionality reduction may not provide computational saving for all nonlinear optimization problems, for some SLAM problems we can achieve benefits such as improvement on time consumption and convergence. For the special case of two-step SLAM when the orientation information from odometry is not incorporated, an algorithm that can guarantee to obtain the globally optimal solution (in the maximum likelihood sense) is derived. Simulation and experimental datasets are used to verify the equivalence between the reduced nonlinear optimization problem and the original full optimization problem, as well as the proposed new algorithm for obtaining the globally optimal solution for two-step SLAM.

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Section: Remarkmentioning

confidence: 98%

Dimensionality reduction for point feature SLAM problems with spherical covariance matrices

et al. 2015

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“…It is worth noting that this approach naturally leads to the minimum mean-square error point estimate (i.e., mean of the posterior instead of its mode). L-SLAM [30,31] uses this idea to exploit the separable structure of feature-based SLAM by employing a RBPF based on a clever partitioning of state variables, i.e., θ vs. p, instead of FastSLAM's choice of poses vs. features [20,21]. Any sequential Monte Carlo method employed on a high-dimensional state space will eventually suffer from degeneracy and consequently, particle depletion [10].…”

Section: Resultsmentioning

confidence: 99%

Exploiting the Separable Structure of SLAM

Khosoussi

Huang

Dissanayake

2015

Robotics: Science and Systems XI

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Abstract-In this paper we point out an overlooked structure of SLAM that distinguishes it from a generic nonlinear least squares problem. The measurement function in most common forms of SLAM is linear with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of state variables can be easily obtained by solving a sparse linear-Gaussian estimation problem. We propose an algorithm to exploit this intrinsic property of SLAM by stripping the problem down to its nonlinear core, while maintaining its natural sparsity. Our algorithm can be used together with any Newton-based iterative solver and is applicable to 2D/3D pose-graph and feature-based problems. Our results suggest that iteratively solving the nonlinear core of SLAM leads to a fast and reliable convergence as compared to the state-of-the-art back-ends.

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“…"Fig 2" illustrates the initial coordinates of the landmark.The azimuth and elevation are given by the equation (8), (9). The initialization includes both the landmark initial values and the covariance matrix which covers a very uncertain depth, but it is well modeled as a Gaussian, and then it can be processed with the standard EKF equation (5). Algorithm 2 summarize the FastSLAM 2.0 …”

Section: Landmark Initializationmentioning

confidence: 99%

“…Therefore SLAM problem is also called concurrent mapping and localization, a good map is needed for localization while an accurate pose estimate is needed to build a map. Several algorithms have been developed to address the problem known as SLAM like EKF-SLAM [1], EIF-SLAM [2], CF-SLAM [3], FastSLAM [4], L-SLAM [5], which aim to enhance the quality of location, robustness and optimize the computational cost. The EKF based approach suffer from two limiting factor.…”

Section: Introductionmentioning

confidence: 99%

FastSLAM 2.0 running on a low-cost embedded architecture

Mohamed

Elouardi

Bouaziz

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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The first method that was developed to deal with the SLAM problem is based on the extended kalman filter, EKF SLAM. However this approach cannot be applied to a large environments because of the quadratic complexity and data association problem. The second approach to address the SLAM problem is based on the Rao-Blackwellized Particle filter FastSLAM, which follows a large number of hypotheses that represent the different possible trajectories, each trajectory carries its own map, its complexity increase logarithmically with the number of landmarks in the map. In this paper we will present the result of an implementation of the FastSLAM 2.0 on an open multimedia applications processor, based on a monocular camera as an exteroceptive sensor. A parallel implementation of this algorithm was achieved. Results aim to demonstrate that an optimized algorithm implemented on a low cost architecture is suitable to design an embedded system for SLAM applications

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L-SLAM: Reduced dimensionality FastSLAM with unknown data association

Cited by 14 publications

References 8 publications

Dimensionality reduction for point feature SLAM problems with spherical covariance matrices

Dimensionality reduction for point feature SLAM problems with spherical covariance matrices

Exploiting the Separable Structure of SLAM

FastSLAM 2.0 running on a low-cost embedded architecture

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