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Real-time dense map fusion for stereo SLAM

Published online by Cambridge University Press:  20 June 2018

Taihú Pire ,
Rodrigo Baravalle ,
Ariel D'Alessandro  and
Javier Civera
Taihú Pire*
Affiliation:
CIFASIS, French Argentine International Center for Information and Systems Sciences (CONICET-UNR), Argentina. E-mails: baravalle@cifasis-conicet.gov.ar, ariel.dalessandro@gmail.com
Rodrigo Baravalle
Affiliation:
CIFASIS, French Argentine International Center for Information and Systems Sciences (CONICET-UNR), Argentina. E-mails: baravalle@cifasis-conicet.gov.ar, ariel.dalessandro@gmail.com
Ariel D'Alessandro
Affiliation:
CIFASIS, French Argentine International Center for Information and Systems Sciences (CONICET-UNR), Argentina. E-mails: baravalle@cifasis-conicet.gov.ar, ariel.dalessandro@gmail.com
Javier Civera
Affiliation:
I3A, University of Zaragoza, Spain. E-mail: jcivera@unizar.es
*
*Corresponding author. E-mail: pire@cifasis-conicet.gov.ar
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Summary

A robot should be able to estimate an accurate and dense 3D model of its environment (a map), along with its pose relative to it, all of it in real time, in order to be able to navigate autonomously without collisions.

As the robot moves from its starting position and the estimated map grows, the computational and memory footprint of a dense 3D map increases and might exceed the robot capabilities in a short time. However, a global map is still needed to maintain its consistency and plan for distant goals, possibly out of the robot field of view.

In this work, we address such problem by proposing a real-time stereo mapping pipeline, feasible for standard CPUs, which is locally dense and globally sparse and accurate. Our algorithm is based on a graph relating poses and salient visual points, in order to maintain a long-term accuracy with a small cost. Within such framework, we propose an efficient dense fusion of several stereo depths in the locality of the current robot pose.

We evaluate the performance and the accuracy of our algorithm in the public datasets of Tsukuba and KITTI, and demonstrate that it outperforms single-view stereo depth. We release the code as open-source, in order to facilitate the system use and comparisons.

Keywords

Dense MappingVisual SLAMStereo Vision
Type
Articles
Information
Robotica , Volume 36 , Issue 10 , October 2018 , pp. 1510 - 1526
Copyright
Copyright © Cambridge University Press 2018 

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