Robust optimization of factor graphs by using condensed measurements

Grisetti, Giorgio; Kümmerle, Rainer; Ni, Kai

doi:10.1109/iros.2012.6385779

Cited by 43 publications

(28 citation statements)

References 21 publications

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“…To improve the performance of Linear SLAM, we recommend to use nonlinear optimization techniques to build high quality small local maps (accurate estimate and small covariance matrix) taking into account the connectivity of the local map graph [43], and then apply our linear map joining algorithm. Furthermore, the solution from Linear SLAM (using least squares to build local maps without marginalization) can be used as an (excellent) initial guess for the iterative approaches to solve the full nonlinear least squares SLAM, similar to T-SAM2 [44] or [39]. However, this will add more nonlinear components in the algorithm.…”

Section: Discussionmentioning

confidence: 99%

Linear SLAM: Linearising the SLAM problems using submap joining

2019

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The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.

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Section: Discussionmentioning

confidence: 99%

Linear SLAM: Linearising the SLAM problems using submap joining

2019

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“…Works on SLAM, for instance, often report the chi-squared test as a measure of solution quality [19] or the optimization objective itself, which is related to the joint observation likelihood [20]. Similarly, observation likelihood is used as an optimization objective in [21] to select Kalman filter parameters.…”

Section: Statistical Metricsmentioning

confidence: 99%

Introspective Evaluation of Perception Performance for Parameter Tuning without Ground Truth

Hu¹,

Kantor²

2017

Robotics: Science and Systems XIII

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Abstract-Modern perception systems are notoriously complex, featuring dozens of interacting parameters that must be tuned to achieve good performance. Conventional tuning approaches require expensive ground truth, while heuristic methods are difficult to generalize. In this work, we propose an introspective ground-truth-free approach to evaluating the performance of a generic perception system. By using the posterior distribution estimate generated by a Bayesian estimator, we show that the expected performance can be estimated efficiently and without ground truth. Our simulated and physical experiments in a demonstrative indoor ground robot state estimation application show that our approach can order parameters similarly to using a ground-truth system, and is able to accurately identify top-performing parameters in varying contexts. In contrast, baseline approaches that reason only about observation log-likelihood fail in the face of challenging perceptual phenomena.

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“…This may be overly restrictive in high-bandwidth systems, but it is necessary in underwater scenarios (where packets are often dropped), since it allows for reasonable scalability of packet sizes. The work of [24] uses the condensed measurement approach introduced in [25]. Specifically, each robot communicates the following information: (i) the last laser scan, (ii) the upto-date estimates of the previous N nodes where N is the number of nodes since the last transmission, (iii) the indexes of the other robots' local maps that they have matched with their own, and (iv) part of the condensed graph computed using the process in [25].…”

Section: A C-slammentioning

confidence: 99%

“…The work of [24] uses the condensed measurement approach introduced in [25]. Specifically, each robot communicates the following information: (i) the last laser scan, (ii) the upto-date estimates of the previous N nodes where N is the number of nodes since the last transmission, (iii) the indexes of the other robots' local maps that they have matched with their own, and (iv) part of the condensed graph computed using the process in [25]. This approach can scale well with respect to the team size, however the packet size is on the order of 2.5 KBytes.…”

Section: A C-slammentioning

confidence: 99%

Communication-constrained multi-AUV cooperative SLAM

Paull¹,

Huang

Seto³

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.

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Robust optimization of factor graphs by using condensed measurements

Cited by 43 publications

References 21 publications

Linear SLAM: Linearising the SLAM problems using submap joining

Linear SLAM: Linearising the SLAM problems using submap joining

Introspective Evaluation of Perception Performance for Parameter Tuning without Ground Truth

Communication-constrained multi-AUV cooperative SLAM

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