Combined visually and geometrically informative link hypothesis for pose-graph visual SLAM using bag-of-words

Kim, Ayoung; Eustice, Ryan M.

doi:10.1109/iros.2011.6094820

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…Combining sonar and visual constants within the same SLAM estimation problem allows for sensor redundancy as well, taking advantage of complementary information. The specific application of visual SLAM to this problem is described in [41].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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Accurate navigation is a fundamental requirement for robotic systemsmarine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely low drift rates, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well as its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes

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Section: Experiments and Resultsmentioning

confidence: 99%

Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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“…Carlevaris-Bianco et al [7]- [10] consolidate densely connected regions of a pose-graph into Generic Linear Constraints (GLCs), while Huang et al [11] use 1 -optimization to consistently remove weak edges in the graph. Sparsification approaches have been used in conjunction with location saliency metrics [12] in order to weight the optimization towards retaining the database entries that are most likely to be recognized [13], but do not explicitly incorporate the likelihood of revisit. Note that all graph sparsification approaches have involved removing or replacing constraints after they have already been added to the graph.…”

Section: Related Workmentioning

confidence: 99%

“…If sample sensor measurements are available for locations in the environment, the location recognition probability can be estimated from a saliency score such as [12], otherwise we assume P (recognize|visit) = 1. An example of the probability of location visit in a street network is shown in Fig.…”

Section: ) Probability Of Visitmentioning

confidence: 99%

Location utility-based map reduction

Steiner¹,

Huang

Leonard³

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Maps used for navigation often include a database of location descriptions for place recognition (loop closing), which permits bounded-error performance. A standard posegraph SLAM system adds a new entry for every new pose into the location database, which grows linearly and unbounded in time and thus becomes unsustainable. To address this issue, in this paper we propose a new map-reduction approach that pre-constructs a fixed-size place-recognition database amenable to the limited storage and processing resources of the vehicle by exploiting the high-level structure of the environment as well as the vehicle motion. In particular, we introduce the concept of location utility -which encapsulates the visitation probability of a location and its spatial distribution relative to nearby locations in the database -as a measure of the value of potential loop-closure events to occur at that location. While finding the optimal reduced location database is NPhard, we develop an efficient greedy algorithm to sort all the locations in a map based on their relative utility without access to sensor measurements or the vehicle trajectory. This enables pre-determination of a generic, limited-size place-recognition database containing the N best locations in the environment. To validate the proposed approach, we develop an open-source street-map simulator using real city-map data and show that an accurate map (pose-graph) can be attained even when using a place-recognition database with only 1% of the entries of the corresponding full database.

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“…Their method was developed for pose graph-based SLAM. Combined with this geometric-derived result, visual information was used in [22]. The richness of the texture in the image captured at each pose was evaluated to determine whether the new pose was informative.…”

Section: Related Workmentioning

confidence: 99%

A pose pruning driven solution to pose feature GraphSLAM

2015

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To build consistent feature-based map for the environment, GraphSLAM forms the graph using the collected information, with poses of robot and features being nodes while the odometry and observations being binary edges (edge links to two nodes). As the number of kept nodes grows unboundedly while robot moves, this method will become intractable for long-duration operation. In this paper, we propose a pose pruning-driven solution for pose feature Simultaneous localization and mapping by relating the size of graph to the size of map instead of the length of trajectory. It consists of two steps: (1) An online pose pruning algorithm that can select a pose to be pruned based on the contribution of the pose. Different from conventional methods considering the spatial distance between poses, the contribution is based on the feature observations of poses, taking mapping into consideration. (2) An edge generation algorithm that can build new consistent binary edges from n-nary edge (edge links to n nodes) induced by marginalizing the pruned pose. The type of new edges remains invariant (i.e. they are either odometry or pose to feature observations), so no extra change is required to be made on the GraphSLAM optimizer, making the proposed solution modular. In the experiment, we first employ this system on simulation data-sets to show how it works. Then the large-scale data-sets: DLR, Victoria Park, and CityTrees10000 are used to evaluate its performance.

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Combined visually and geometrically informative link hypothesis for pose-graph visual SLAM using bag-of-words

Cited by 13 publications

References 33 publications

Simultaneous Localization and Mapping in Marine Environments

Simultaneous Localization and Mapping in Marine Environments

Location utility-based map reduction

A pose pruning driven solution to pose feature GraphSLAM

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