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11
Consistent sparsification for graph optimization
- in Proc. European Conf. Mobile Robotics
, 2013
"... Abstract — In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the ..."
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Cited by 8 (1 self)
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Abstract — In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the pose graph amenable to available processing and memory resources. In particular, in this paper we introduce a consistent graph sparsification scheme: i) sparsifying nodes via marginalization of old nodes, while retaining all the information (consistent relative constraints) – which is conveyed in the discarded measurements – about the remaining nodes after marginalization; and ii) sparsifying edges by formulating and solving a consistentℓ1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data. I.
Generic Node Removal for Factor-Graph SLAM
, 2014
"... 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 ..."
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Cited by 5 (3 self)
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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 a way that accounts for measurement correlation, 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 h of real-world indoor-outdoor data covering 147.4 km collected over 27 mapping sessions spanning a period of 15 months.
Long-Term Simultaneous Localization and Mapping with Generic Linear Constraint Node Removal
"... Abstract — This paper reports on the use of generic linear constraint (GLC) node removal as a method to control the computational complexity of long-term simultaneous localization and mapping. We experimentally demonstrate that GLC provides a principled and flexible tool enabling a wide variety of c ..."
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Abstract — This paper reports on the use of generic linear constraint (GLC) node removal as a method to control the computational complexity of long-term simultaneous localization and mapping. We experimentally demonstrate that GLC provides a principled and flexible tool enabling a wide variety of complexity management schemes. Specifically, we consider two main classes: batch multi-session node removal, in which nodes are removed in a batch operation between mapping sessions, and online node removal, in which nodes are removed as the robot operates. Results are shown for 34.9 h of realworld indoor-outdoor data covering 147.4 km collected over 27 mapping sessions spanning a period of 15 months.
An Origin State Method for Communication Constrained Cooperative Localization with Robustness to Packet Loss
"... This paper reports on an exact, real-time solution for server-client cooperative localization over a faulty and ex-tremely bandwidth-limited underwater communication channel. Our algorithm, termed the origin state method, enables a ‘server ’ vehicle to broadcast its navigation information to multipl ..."
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Cited by 3 (2 self)
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This paper reports on an exact, real-time solution for server-client cooperative localization over a faulty and ex-tremely bandwidth-limited underwater communication channel. Our algorithm, termed the origin state method, enables a ‘server ’ vehicle to broadcast its navigation information to multiple ‘client ’ vehicles over a bandwidth-limited and faulty communication channel. The server’s broadcasted pose-graph can be used in conjunction with an estimator on the client to exactly reproduce the corresponding server-client centralized estimate. We present an evaluation over an extensive real-time field implementation of the proposed algorithm for a multi-agent autonomous underwater vehicle network using underwater acoustic modems to communicate in a synchronous-clock transmis-sion framework. 1
Conservative Edge Sparsification for Graph SLAM Node Removal
"... Abstract—This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow-Liu tr ..."
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Abstract—This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow-Liu tree approximation of the marginalization potential and then use optimization-based methods to adjust the approximation so that it is conservative subject to minimizing the Kullback-Leibler divergence (KLD) from the true marginalization potential. Re-sults are presented over multiple real-world SLAM graphs and show that the proposed methods enforce a conservative approxi-mation, while achieving low KLD from the true marginalization potential. I.
Long-term mapping techniques for ship hull inspection and surveillance using an autonomous underwater vehicle
- J. Field Robot
"... This paper reports on a system for an autonomous underwater vehicle to perform in-situ, multiple session, hull inspection using long-term simultaneous localization and mapping (SLAM). Our method assumes very little a-priori knowledge, and does not require the aid of acoustic beacons for navigation, ..."
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Cited by 2 (2 self)
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This paper reports on a system for an autonomous underwater vehicle to perform in-situ, multiple session, hull inspection using long-term simultaneous localization and mapping (SLAM). Our method assumes very little a-priori knowledge, and does not require the aid of acoustic beacons for navigation, which is a typical mode of navigation in this type of ap-plication. Our system combines recent techniques in underwater saliency-informed visual SLAM and a method for representing the ship hull surface as a collection of many locally planar surface features. This methodology produces accurate maps that can be constructed in real-time on consumer-grade computing hardware. A single-session SLAM result is ini-tially used as a prior map for later sessions, where the robot automatically merges the mul-tiple surveys into a common hull-relative reference frame. To perform the re-localization step, we use a particle filter that leverages the locally planar representation of the ship hull surface, and a fast visual descriptor matching algorithm. Finally, we apply the recently-developed graph sparsification tool, generic linear constraints (GLC), as a way to manage the computational complexity of the SLAM system as the robot accumulates information across multiple sessions. We show results for 20 SLAM sessions for two large vessels over the course of days, months, and even up to three years, with a total path length of approx-imately 10.2 km. 1
Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors
"... Abstract — Factor graphs are a general estimation framework that has been widely used in computer vision and robotics. In several classes of problems a natural partition arises among variables involved in the estimation. A subset of the variables are actually of interest for the user: we call those ..."
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Abstract — Factor graphs are a general estimation framework that has been widely used in computer vision and robotics. In several classes of problems a natural partition arises among variables involved in the estimation. A subset of the variables are actually of interest for the user: we call those target variables. The remaining variables are essential for the for-mulation of the optimization problem underlying maximum a posteriori (MAP) estimation; however these variables, that we call support variables, are not strictly required as output of the estimation problem. In this paper, we propose a systematic way to abstract support variables, defining optimization problems that are only defined over the set of target variables. This abstraction naturally leads to the definition of smart factors, which correspond to constraints among target variables. We show that this perspective unifies the treatment of heteroge-neous problems, ranging from structureless bundle adjustment to robust estimation in SLAM. Moreover, it enables to exploit the underlying structure of the optimization problem and the treatment of degenerate instances, enhancing both computa-tional efficiency and robustness. I.
iSPCG: Incremental Subgraph-Preconditioned Conjugate Gradient Method for Online SLAM with Many Loop-Closures
"... Abstract — We propose a novel method to solve online SLAM problems with many loop-closures on the basis of two state-of-the-art SLAM methods, iSAM and SPCG. We first use iSAM to solve a sparse sub-problem to obtain an approximate solution. When the error grows larger than a threshold or the optimal ..."
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Abstract — We propose a novel method to solve online SLAM problems with many loop-closures on the basis of two state-of-the-art SLAM methods, iSAM and SPCG. We first use iSAM to solve a sparse sub-problem to obtain an approximate solution. When the error grows larger than a threshold or the optimal solution is requested, we use subgraph-preconditioned conjugate gradient method to solve the original problem where the subgraph preconditioner and initial estimate are provided by iSAM. Finally we use the optimal solution from SPCG to regularize iSAM in the next steps. The proposed method is consistent, efficient and can find the optimal solution. We apply this method to solve large simulated and real SLAM problems, and obtain promising results. I.
Long-Term Simultaneous Localization and Mapping in Dynamic Environments
, 2015
"... This thesis was made possible by: least squares; my wife Amy and my parents Chris and ..."
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This thesis was made possible by: least squares; my wife Amy and my parents Chris and
Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks
, 2015
"... Instead of ‘Acknowledgments’, this section heading should really read ‘Coauthors’. I would not have written a single page if not for the enormous amount of help and support I have received from so many people (more than can be listed below). Knowing that the following cannot completely suffice, I’ll ..."
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Instead of ‘Acknowledgments’, this section heading should really read ‘Coauthors’. I would not have written a single page if not for the enormous amount of help and support I have received from so many people (more than can be listed below). Knowing that the following cannot completely suffice, I’ll begin with a big ‘thank you ’ to all. First, Ryan, thank you for taking me on as a student six years ago. Thank you for trusting me to throw your AUVs over the side of a boat, and trusting that they would be returned. I’m glad that research has always been one part what we can show on paper, one part what we can demonstrate on real hardware. Hopefully, years from now, you’ll still think that time Paul sunburned his hands was pretty funny. Next, to Professor Barton, Professor Grizzle, and Professor Whitcomb, thank you for serving on my committee. In particular, Louis, you provided an example of scholarship over the last five years that I have tried to emulate. Moreover, for what it counts coming from me, you do some really cool work in some really cool places. To all PeRL members and affiliates, in order of appearance, Ayoung, Gaurav, Nick (glad you have a new desk), Paul (of sunburned hand fame), Sarah, Sweets (Chaves), Wolcott, Schuyler,
