Efficient Merging of Maps and Detection of Changes

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.

Original languageEnglish
Title of host publicationImage Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
EditorsMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
Number of pages13
ISBN (Print)9783030202040
Publication statusPublished - 2019
Event21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sweden
Duration: 2019 Jun 112019 Jun 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11482 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st Scandinavian Conference on Image Analysis, SCIA 2019

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)

Free keywords

  • Change detection
  • Collaborative SLAM
  • Map merging
  • SfM


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