Toward multidimensional assignment data association in robot localization and mapping

Wijesoma, W.S.; Perera, L.D.L.; Adams, Martin

doi:10.1109/tro.2006.870634

Cited by 56 publications

(62 citation statements)

References 15 publications

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“…Feature based SLAM data association algorithm includes Nearest Neighbor (NN) [4], Multi-Hypothesis Tracker (MHT) [5], Joint Compatibility Branch and Bound (JCBB) [6], Multidimensional Assignment (MDA) [7], and Highest Order Hypothesis Compatibility Test (HOHCT) [8], etc. The NN uses mean square distance as the threshold criterion.…”

Section: Filtering Data Associationmentioning

confidence: 99%

Review of SLAM Data Association Study

Wu¹,

Shiju²,

Cao³

et al. 2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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Abstract. Simultaneous localization and map building (SLAM) is a key problem for an intelligent robot to accomplish autonomous navigation. And the results of data association are of key importance to the success of the SLAM. In this study, two bottleneck issues constraining SLAM data association are discussed in detail. One issue is the filtering data association of SLAM, the other issue is the loop closure of SLAM. The research status of the two issues is explained in detail. On this basis, the inadequacies of existing research are pointed out. And some advices for future research are provided. The work of this paper is of a certain reference value for both comparison of existing data association studies in SLAM and future intensive SLAM research.

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Section: Filtering Data Associationmentioning

confidence: 99%

Review of SLAM Data Association Study

Wu¹,

Shiju²,

Cao³

et al. 2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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show abstract

“…An effective scheme of matching must make distinctions between spurious measurements, new measurements and lost measurements together with a basic function to associate the available map with the new measurements. A study of the techniques more used to solve the matching problem can be found in the work of Wijesoma et al [8], where are presented the general idea of solutions and their respective original references.…”

Section: Matchingmentioning

confidence: 99%

3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision

Souza¹,

Maia²,

Gonçalves³

2012

Current Advancements in Stereo Vision

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“…Although data association has been studied extensively in the context of target tracking applications, such as in [6], it has also become an increasingly important field of research for localization applications. For example, due to its repeated requirement to correspond sensor measurements with features in the environment, the SLAM problem is fundamentally akin to a multisensor multitarget tracking problem [92]. More generally, navigation is essentially the process of continually estimating a vehicle's position with periodic updates from an external navigational aid to limit the growth of position estimate error.…”

Section: Data Association Techniquesmentioning

confidence: 99%

A parallel hypothesis method of autonomous underwater vehicle navigation

LaPointe¹

2009

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This research presents a parallel hypothesis method for autonomous underwater vehicle navigation that enables a vehicle to expand the operating envelope of existing long baseline acoustic navigation systems by incorporating information that is not normally used. The parallel hypothesis method allows the in-situ identification of acoustic multipath time-of-flight measurements between a vehicle and an external transponder and uses them in real-time to augment the navigation algorithm during periods when direct-path time-of-flight measurements are not available. A proof of concept was conducted using real-world data obtained by the Woods Hole Oceanographic Institution Deep Submergence Lab's Autonomous Benthic Explorer (ABE) and Sentry autonomous underwater vehicles during operations on the Juan de Fuca Ridge.This algorithm uses a nested architecture to break the navigation solution down into basic building blocks for each type of available external information. The algorithm classifies external information as either line of position or gridded observations. For any line of position observation, the algorithm generates a multi-modal block of parallel position estimate hypotheses. The multimodal hypotheses are input into an arbiter which produces a single unimodal output. If a priori maps of gridded information are available, they are used within the arbiter structure to aid in the elimination of false hypotheses. For the proof of concept, this research uses ranges from a single external acoustic transponder in the hypothesis generation process and grids of low-resolution bathymetric data from a ship-based multibeam sonar in the arbitration process.The major contributions of this research include the in-situ identification of acoustic multipath time-of-flight measurements, the multiscale utilization of a priori lowresolution bathymetric data in a high-resolution navigation algorithm, and the design of a navigation algorithm with a flexible architecture. This flexible architecture allows 3 the incorporation of multimodal beliefs without requiring a complex mechanism for real-time hypothesis generation and culling, and it allows the real-time incorporation of multiple types of external information as they become available in situ into the overall navigation solution.

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Toward multidimensional assignment data association in robot localization and mapping

Cited by 56 publications

References 15 publications

Review of SLAM Data Association Study

Review of SLAM Data Association Study

3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision

A parallel hypothesis method of autonomous underwater vehicle navigation

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