Square Root SAM: Simultaneous Localization and Mapping via Square Root                 Information Smoothing

Dellaert, Frank; Kaess, Michael

doi:10.1177/0278364906072768

Cited by 789 publications

(779 citation statements)

References 55 publications

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“…Due to the Gaussian assumption on the observation and odometry noises, the ML estimation problem is equivalent to a least squares formulation [6]. The least squares problem can be formulated using (4) and (9) similar to that in [6].…”

Section: B Build Local Map Using MLmentioning

confidence: 99%

“…Email: {gibson.hu, sdhuang, gdissa}@eng.uts.edu.au 1 In this paper, the ML approach means to find the maximal likelihood of all the robot poses and all the observed feature positions using all the information available. It is also called Smoothing and Mapping (SAM) [6].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some efficient graph optimization based approach [6][10] are developed using the sparse graph representations of the SLAM problem. Optimization based approach can provide more consistent estimate as compared with the filter based approach.…”

Section: Introductionmentioning

confidence: 99%

“…Optimization based approach can provide more consistent estimate as compared with the filter based approach. However, a major issue of these approaches is that the state dimension is very high because all the robot poses are included [6][10][14] [17].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map

Huang

Dissanayake

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Section: B Build Local Map Using MLmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map

Huang

Dissanayake

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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“…It exploits the sparseness inherent in the structure-frommotion problem to reduce the complexity. Without maintaining all landmark descriptors in this manner one could even use a more efficient sparse matrix system [19] [20] [8], [13], [14] as the back end to build a globally consistent map. As efficient as sparse matrix methods are, they still have limitations and aren't used to process all frames of video as this would generate much denser graphs with high connectivity which would overwhelm the approaches.…”

Section: Introductionmentioning

confidence: 99%

Reduced Dimensionality Extended Kalman Filter for SLAM

Gamage¹,

Drummond²

2013

Procedings of the British Machine Vision Conference 2013

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Computational complexity of the Kalman filter grows at least quadratically with the number of dimensions in the filter. This is a particular problem for applications like monocular simultaneous localization and mapping (SLAM) where it is not possible to run a single filter on a large map with many thousands of landmarks.This paper presents a method for dramatically reducing the computational complexity of the Kalman filters by reducing the dimensionality as information is acquired. We prove the validity of our method by applying it to monocular SLAM, where there is a large number of dimensions in the filter that are not subject to process noise (the landmark locations). This has the effect of reducing the cost of running a filter or allowing a single filter to process a much larger set of landmarks.Our approach also has a role to play within modern efficient sparse matrix approaches to SLAM where local information is coalesced into keyframes using Kalman filters. It also has general applicability to filtered measurement of static quantities where there are large numbers of dimensions that are not subject to process noise.

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Robot Localization: An Introduction

Huang

Dissanayake

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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Robot localization is the process of determining where a mobile robot is located with respect to its environment. Localization is one of the most fundamental competencies required by an autonomous robot as the knowledge of the robot's own location is an essential precursor to making decisions about future actions. In a typical robot localization scenario, a map of the environment is available and the robot is equipped with sensors that observe the environment as well as monitor its own motion. The localization problem then becomes one of estimating the robot position and orientation within the map using information gathered from these sensors. Robot localization techniques need to be able to deal with noisy observations and generate not only an estimate of the robot location but also a measure of the uncertainty of the location estimate. This article provides an introduction to estimation of theoretic solutions to the robot localization problem. It begins by discussing the mathematical models used to describe the robot motion and observations from the sensors. Two of the most common probabilistic techniques, the extended Kalman filter and the particle filter, that can be used to combine information from sensors to compute an estimate of the robot location are then discussed in detail and illustrated by simple examples. A brief summary of the large body of literature on robot localization is presented next. Appendices that present the essential mathematical background and alternative techniques are provided. The MATLAB code of the localization algorithms is also available.

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Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

Abstract: Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot

Cited by 789 publications

References 55 publications

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map

Reduced Dimensionality Extended Kalman Filter for SLAM

Robot Localization: An Introduction

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