Monocular graph SLAM with complexity reduction

Eade, Ethan; Fong, Philip W. L.; Munich, Mario E.

doi:10.1109/iros.2010.5649205

Cited by 58 publications

(51 citation statements)

References 17 publications

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“…In [15], nodes that provide the least information to an occupancy grid are removed. Nodes without associated imagery are removed in [14]. Finally, in [16], "inactive" nodes that no longer contribute to the laser-based map (because the environment has changed) are removed.…”

Section: Introductionmentioning

confidence: 99%

Long-term simultaneous localization and mapping with generic linear constraint node removal

Carlevaris-Bianco

Eustice

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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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. I. INTRODUCTIONGraph-based simultaneous localization and mapping (SLAM) [1]- [7] has been used to successfully solve many challenging SLAM problems in robotics. In graph SLAM, the problem of finding the optimal configuration of historic robot poses (and optionally the location of landmarks), is associated with a Markov random field or factor graph. In the factor graph representation, robot poses are represented by nodes and measurements between nodes by factors. Under the assumption of Gaussian measurement noise the graph represents a least squares optimization problem. The computational complexity of this problem is dictated by the density of connectivity within the graph, and by the number of nodes and factors it contains.Unfortunately, the standard formulation of graph SLAM requires that nodes be continually added to the graph for localization. This is a problem for long-term applications as the computational complexity of the graph becomes dependent not only on the spatial extent of the environment, but also the duration of the exploration (Fig. 1(b)).Early filtering-based works [8], [9], and more recently [10], have focused on controlling the computational complexity by enforcing sparse connectivity in the graph. In [11], an information-theoretic approach is used to slow the rate of the graph growth by avoiding the addition of uninformative poses. In [12], when the robot revisits a previously explored location, it avoids adding new nodes and instead adds links between existing nodes.

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Section: Introductionmentioning

confidence: 99%

Long-term simultaneous localization and mapping with generic linear constraint node removal

Carlevaris-Bianco

Eustice

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…An algorithm for complexity reduction was proposed by Eade et al [17]. The reduction consists of marginalization and degree thresholding.…”

Section: Approaches To Lifelong Mappingmentioning

confidence: 99%

“…Another approach is taken by Eade et al [17] where the marginal distribution is approximated by using the existing measurements to construct new connections between all the nodes that are neighbors to the node to be removed. Instead of reducing the marginal distribution during construction, edge pruning is used.…”

Section: Graph Reductionmentioning

confidence: 99%

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Toward lifelong visual localization and mapping

Johannsson¹

2013

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Mobile robotic systems operating over long durations require algorithms that are robust and scale efficiently over time as sensor information is continually collected. For mobile robots one of the fundamental problems is navigation; which requires the robot to have a map of its environment, so it can plan its path and execute it. Having the robot use its perception sensors to do simultaneous localization and mapping (SLAM) is beneficial for a fully autonomous system. Extending the time horizon of operations poses problems to current SLAM algorithms, both in terms of robustness and temporal scalability. To address this problem we propose a reduced pose graph model that significantly reduces the complexity of the full pose graph model. Additionally we develop a SLAM system using two different sensor modalities: imaging sonars for underwater navigation and vision based SLAM for terrestrial applications.Underwater navigation is one application domain that benefits from SLAM, where access to a global positioning system (GPS) is not possible. In this thesis we present SLAM systems for two underwater applications. First, we describe our implementation of real-time imaging-sonar aided navigation applied to in-situ autonomous ship hull inspection using the hovering autonomous underwater vehicle (HAUV). In addition we present an architecture that enables the fusion of information from both a sonar and a camera system. The system is evaluated using data collected during experiments on SS Curtiss and USCGC Seneca. Second, we develop a feature-based navigation system supporting multi-session mapping, and provide an algorithm for re-localizing the vehicle between missions. In addition we present a method for managing the complexity of the estimation problem as new information is received. The system is demonstrated using data collected with a REMUS vehicle equipped with 3 a BlueView forward-looking sonar.The model we use for mapping builds on the pose graph representation which has been shown to be an efficient and accurate approach to SLAM. One of the problems with the pose graph formulation is that the state space continuously grows as more information is acquired. To address this problem we propose the reduced pose graph (RPG) model which partitions the space to be mapped and uses the partitions to reduce the number of poses used for estimation. To evaluate our approach, we present results using an online binocular and RGB-Depth visual SLAM system that uses place recognition both for robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping using approximately nine hours of data collected in the MIT Stata Center over the course of six months. Ground truth, derived by aligning laser scans to existing floor plans, is used to evaluate the global accuracy of the system. Our results illustrate the capability of our visual SLAM system to map a large scale environment over an exte...

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“…Even though the issue of reducing the complexity of graph optimization, in particular, for SLAM, has recently been addressed [5]- [9], to the best of our knowledge, little work has yet explicitly taken into account estimation consistency (i.e., unbiased estimates, and the estimated covariance larger than or equal to the true covariance [10]) in the design of graph reduction (sparsification) scheme. This is a significant limitation, since if an estimator is inconsistent, then the accuracy of the computed state estimates is unknown, which in turn makes the estimator unreliable.…”

Section: Introductionmentioning

confidence: 99%

Consistent sparsification for graph optimization

Huang

Kaess

Leonard

2013

2013 European Conference on Mobile Robots

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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 consistent ℓ1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data.

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Monocular graph SLAM with complexity reduction

Cited by 58 publications

References 17 publications

Long-term simultaneous localization and mapping with generic linear constraint node removal

Long-term simultaneous localization and mapping with generic linear constraint node removal

Toward lifelong visual localization and mapping

Consistent sparsification for graph optimization

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