Category Archives: Mobile Robot Slam

Robotic probabilistic SLAM in continuous time

December 22, 2015 19:43 ,

Furgale1 P., Tong C.-H., Barfoot T.-D., Sibley G., Continuous-time batch trajectory estimation using temporal basis functions, The International Journal of Robotics Research December 2015 vol. 34 no. 14 1688-1710, DOI: 10.1177/0278364915585860.

Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable; however, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units, rolling-shutter cameras, or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full Maximum-a-Posteriori estimation problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian and show how the coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms through the application of different assumptions at any point. We use the simultaneous localization and mapping problem as our motivation throughout the paper, although the approach is not specific to this application. Results from two experiments are provided to validate the approach: (i) self-calibration involving a camera and a high-rate inertial measurement unit, and (ii) perspective localization with a rolling-shutter camera.

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A very interesting review of current approaches to SLAM based on smoothing (i.e., graph optimization) and in clustering the map into submaps

July 22, 2015 09:37 ,

Jiantong Cheng, Jonghyuk Kim, Jinliang Shao, Weihua Zhang, Robust linear pose graph-based SLAM, Robotics and Autonomous Systems, Volume 72, October 2015, Pages 71-82, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.04.010.

This paper addresses a robust and efficient solution to eliminate false loop-closures in a pose-graph linear SLAM problem. Linear SLAM was recently demonstrated based on submap joining techniques in which a nonlinear coordinate transformation was performed separately out of the optimization loop, resulting in a convex optimization problem. This however introduces added complexities in dealing with false loop-closures, which mostly stems from two factors: (a) the limited local observations in map-joining stages and (b) the non block-diagonal nature of the information matrix of each submap. To address these problems, we propose a Robust Linear SLAM by (a) developing a delayed optimization for outlier candidates and (b) utilizing a Schur complement to efficiently eliminate corrupted information block. Based on this new strategy, we prove that the spread of outlier information does not compromise the optimization performance of inliers and can be fully filtered out from the corrupted information matrix. Experimental results based on public synthetic and real-world datasets in 2D and 3D environments show that this robust approach can cope with the incorrect loop-closures robustly and effectively.

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A nice SLAM approach based on hybrid Normal Distribution Transform (NDT) + occupancy grid maps intended for long term operation in dynamic environments

May 4, 2015 10:23 ,

Erik Einhorn, Horst-Michael Gross, Generic NDT mapping in dynamic environments and its application for lifelong SLAM, Robotics and Autonomous Systems, Volume 69, July 2015, Pages 28-39, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.08.008.

In this paper, we present a new, generic approach for Simultaneous Localization and Mapping (SLAM). First of all, we propose an abstraction of the underlying sensor data using Normal Distribution Transform (NDT) maps that are suitable for making our approach independent from the used sensor and the dimension of the generated maps. We present several modifications for the original NDT mapping to handle free-space measurements explicitly. We additionally describe a method to detect and handle dynamic objects such as moving persons. This enables the usage of the proposed approach in highly dynamic environments. In the second part of this paper we describe our graph-based SLAM approach that is designed for lifelong usage. Therefore, the memory and computational complexity is limited by pruning the pose graph in an appropriate way.

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A survey on topological localization and mapping

December 19, 2014 10:13 ,

Emilio Garcia-Fidalgo, Alberto Ortiz, Vision-based topological mapping and localization methods: A survey , Robotics and Autonomous Systems, Volume 64, February 2015, Pages 1-20, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.11.009

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Topological maps model the environment as a graph, where nodes are distinctive places of the environment and edges indicate topological relationships between them. They represent an interesting alternative to the classic metric maps, due to their simplicity and storage needs, what has made topological mapping and localization an active research area. The different solutions that have been proposed during years have been designed around several kinds of sensors. However, in the last decades, vision approaches have emerged because of the technology improvements and the amount of useful information that a camera can provide. In this paper, we review the main solutions presented in the last fifteen years, and classify them in accordance to the kind of image descriptor employed. Advantages and disadvantages of each approach are thoroughly reviewed and discussed.

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Good related work of graph-based SLAM algorithms that employ some reduction technique on the graph to improve long-term operation, and proposal of a new method of reduction

December 9, 2014 17:30 ,

Carlevaris-Bianco, N.; Kaess, M.; Eustice, R.M., Generic Node Removal for Factor-Graph SLAM, Robotics, IEEE Transactions on , vol.30, no.6, pp.1371,1385, Dec. 2014. DOI: 10.1109/TRO.2014.2347571

This paper reports on a generic factor-based method for node removal in factor-graph simultaneous localization and mapping (SLAM), which we call generic linear constraints (GLCs). The need for a generic node removal tool is motivated by long-term SLAM applications, whereby nodes are removed in order to control the computational cost of graph optimization. GLC is able to produce a new set of linearized factors over the elimination clique that can represent either the true marginalization (i.e., dense GLC) or a sparse approximation of the true marginalization using a Chow-Liu tree (i.e., sparse GLC). The proposed algorithm improves upon commonly used methods in two key ways: First, it is not limited to graphs with strictly full-state relative-pose factors and works equally well with other low-rank factors, such as those produced by monocular vision. Second, the new factors are produced in such a way that accounts for measurement correlation, which is a problem encountered in other methods that rely strictly upon pairwise measurement composition. We evaluate the proposed method over multiple real-world SLAM graphs and show that it outperforms other recently proposed methods in terms of Kullback–Leibler divergence. Additionally, we experimentally demonstrate that the proposed GLC method provides a principled and flexible tool to control the computational complexity of long-term graph SLAM, with results shown for ${34.9}, {rm {h}}$ of real-world indoor–outdoor data covering ${147.4}{hbox{ km}}$ collected over $27$ mapping sessions spanning a period of $15$ months.

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SLAM as a least-squares optimization problem and reduction of the cost through the use of spherical covariance matrices that approximate the original, sparse ones

December 9, 2014 17:09 ,

Heng Wang, Shoudong Huang, Kasra Khosoussi, Udo Frese, Gamini Dissanayake, Bingbing Liu, Dimensionality reduction for point feature SLAM problems with spherical covariance matrices, Automatica, Volume 51, January 2015, Pages 149-157, ISSN 0005-1098. DOI: 10.1016/j.automatica.2014.10.114

The 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 3 m + 2 n 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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