Implementation Issues and Experimental Evaluation of D-SLAM

Wang, Zhan; Huang, Shoudong; Dissanayake, Gamini

doi:10.1007/978-3-540-33453-8_14

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…Since information about robot poses and their relation with features is not used in the mapping task, this leads to information loss in mapping. An extension to D-SLAM (Wang et al 2006) uses absolute feature location rather than relative location.…”

Section: 1b Information Filtersmentioning

confidence: 99%

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Dhiman

Deodhare

Khemani

2015

Sadhana

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This paper presents a survey of Simultaneous Localization And Mapping (SLAM) algorithms for unmanned ground robots. SLAM is the process of creating a map of the environment, sometimes unknown a priori, while at the same time localizing the robot in the same map. The map could be one of different types i.e. metrical, topological, hybrid or semantic. In this paper, the classification of algorithms is done in three classes: (i) Metric map generating approaches, (ii) Qualitative map generating approaches, and (iii) Hybrid map generating approaches. SLAM algorithms for both static and dynamic environments have been surveyed. The algorithms in each class are further divided based on the techniques used. The survey in this paper presents the current state-of-the-art methods, including important landmark works reported in the literature.

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Section: 1b Information Filtersmentioning

confidence: 99%

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Dhiman

Deodhare

Khemani

2015

Sadhana

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“…The results are presented in Figure 3. Although the robot localization estimates are conservative compared to traditional SLAM, the new algorithm provided a much superior estimate to that presented in [16].…”

Section: 1 Experimental Evaluation With a Pioneer Robot In An Officmentioning

confidence: 93%

“…This paper presents an extension of the decoupled SLAM algorithm, D-SLAM, proposed by the authors in (15) (16). where SLAM is reformulated as a static estimation problem for mapping and a three dimensional dynamic estimation problem for localization.…”

Section: Introductionmentioning

confidence: 99%

“…where SLAM is reformulated as a static estimation problem for mapping and a three dimensional dynamic estimation problem for localization. The landmark locations are maintained using either a compact relative map (15) or an absolute Cartesian map [16). The new formulation retains the significant advantage of being able to improve the location estimates of all the landmarks from one local observation.…”

Section: Introductionmentioning

confidence: 99%

“…yet results in an exactly sparse information matrix with the number of nonzero elements related to the range of the sensor on board the robot. The main assumption in [15] [16) is that the robot can observe at least two previously seen landmarks in each observation. This paper provides a strategy to relax the above assumption by merging a set of observations to construct admissible measurements.…”

Section: Introductionmentioning

confidence: 99%

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D-SLAM: Decoupled Localization and Mapping for Autonomous Robots

Wang

Huang

Dissanayake

Springer Tracts in Advanced Robotics

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Summary. The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments.

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Decoupling localization and mapping in SLAM using compact relative maps

Wang

Huang

Dissanayake

2005

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In this paper, we propose a new algorithm for SLAM that makes use of a state vector consisting of quantities that describes the relative locations among features. In contrast to previous relative map strategies, the new state vector is compact and always consists of 2n − 3 elements (in a 2-D environment) where n is the number of features in the map. It is also shown that the information from observations can be transformed and grouped into two parts: first one containing the information about the map and the second one containing the information about the robot location relative to the features in the map. Therefore the SLAM can be decoupled into two processes where mapping uses the first part of the transformed observation vector and localization becomes a 3-dimensional estimation problem. It is also shown that the information matrix of the map is exactly sparse, resulting in potential computational savings when an information filter is used for mapping. The new decoupled SLAM algorithm is called D-SLAM and is illustrated using simulation.

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Implementation Issues and Experimental Evaluation of D-SLAM

Cited by 5 publications

References 11 publications

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

D-SLAM: Decoupled Localization and Mapping for Autonomous Robots

Decoupling localization and mapping in SLAM using compact relative maps

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