SLAM++-A Highly Efficient and Temporally Scalable Incremental SLAM Framework

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU121645" target="_blank" >RIV/00216305:26230/17:PU121645 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1177/0278364917691110" target="_blank" >https://doi.org/10.1177/0278364917691110</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/0278364917691110" target="_blank" >10.1177/0278364917691110</a>

Result language

angličtina

Original language name

SLAM++-A Highly Efficient and Temporally Scalable Incremental SLAM Framework

Original language description

The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation (MLE) is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the MLE converts to a nonlinear least squares (NLS) problem. Efficient solutions to NLS exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localisation and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental MLE called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in NLS. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

ISSN

0278-3649

e-ISSN

1741-3176

Volume of the periodical

2017

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

21

Pages from-to

210-230

UT code for WoS article

000399558300006

EID of the result in the Scopus database

2-s2.0-85018786448