Robust simultaneous localization and mapping via information matrix estimation

Graham, Matthew; How, Jonathan P.

doi:10.1109/plans.2014.6851458

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The algorithm then selects the most likely mixture component before each update. Finally, several robust algorithms have been proposed that add additional variables to the robust SLAM problem and solve the augmented problem by applying the Expectation-Maximization algorithm [9,10].…”

Section: B Robust Slam Algorithmsmentioning

confidence: 99%

Robust incremental SLAM with consistency-checking

Graham

How

Gustafson

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Both landmark measurements and loop closures are used to correct for odometry drift in SLAM solutions. However, if any of the measurements are incorrect (e.g. due to perceptual aliasing) standard SLAM algorithms fail catastrophically and can not return an accurate map. A number of algorithms have been proposed that are robust to loop closure errors, but it is shown in this paper that they can not provide robust solutions when landmark measurement errors occur. The root cause of the problem is that most robust SLAM algorithms only focus on creating a locally consistent map (by evaluating whether measurements appear correct individually) rather than a globally consistent map. This paper proposes a new formulation of the robust SLAM problem that explicitly requires finding a globally consistent solution. Motivated by the new cost function, a novel incremental SLAM algorithm is developed that provides accurate solutions for datasets with landmark or loop closure measurement errors. Simulated and experimental results of the new algorithm, called incremental SLAM with consistency-checking, show that the new algorithm provides significantly more accurate results than state-of-theart robust SLAM methods for datasets with incorrect landmark measurements and can match the performance of current robust SLAM methods for datasets with incorrect loop closures.

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Section: B Robust Slam Algorithmsmentioning

confidence: 99%

Robust incremental SLAM with consistency-checking

Graham

How

Gustafson

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

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“…Thus, the need for a cheap and efficient implementation with cheaper data acquisition methods [9] and faster data processing algorithms [10] is of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

Robotic Mapping Using Autonomous Vehicle

Mahadevaswamy¹,

Keshava²,

Lamani³

et al. 2020

SN COMPUT. SCI.

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Mapping the interior of the building is the first step in order to obtain the description of that building. In this work, an autonomous robot is designed and built. The robot is able to navigate through an unknown environment, avoiding obstacles and simultaneously build a 3D map on its own. This autonomous feature is implemented using minimal and economically viable hardware such as Kinect v2 sensor and Arduino. Also, an open-source platform Robotic Operating System is used, on top of which the mapping and navigation algorithms are implemented. The 3D map built can be used with a lot of existing devices which involves localization. Post-processing procedures are used to obtain point clouds for building the 3D map. Emphasis is put on the efficient use of the available resources to generate the 3D maps of unknown indoor environment.

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“…This line of work can be regarded as a variant/extension of M-estimators, as discussed in [19] [20]. Since NLS SLAM formulation aims to minimize the residual error weighted by a covariance, the second line of work [21] [20] [22] uses the covariance explicitly to control the impact of outliers. In this sense, an outlier is regarded as the measurement extremely inconsistent with the noise model, thus the estimation algorithm adapts the covariance to make it fit the real measurement noise.…”

mentioning

confidence: 99%

Robust Incremental SLAM Under Constrained Optimization Formulation

Bai

Vidal-Calleja

Huang

2018

IEEE Robot. Autom. Lett.

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In this paper, we propose a constrained optimization formulation of SLAM and a robust incremental SLAM framework. The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loopclosure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima, and perform χ 2 difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric, and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost.

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Robust simultaneous localization and mapping via information matrix estimation

Cited by 7 publications

References 12 publications

Robust incremental SLAM with consistency-checking

Robust incremental SLAM with consistency-checking

Robotic Mapping Using Autonomous Vehicle

Robust Incremental SLAM Under Constrained Optimization Formulation

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