O-POCO: Online point cloud compression mapping for visual odometry and SLAM

Luis Angel Contreras Toledo, Walterio Mayol-Cuevas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)
558 Downloads (Pure)

Abstract

This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and compress the map. We propose and evaluate a number of different information layers such as the descriptor information's relative entropy, map-feature occupancy grid, and the point cloud's geometry error. We compare our proposed system against both SfM, and online and offline ORB-SLAM using publicly available datasets in addition to our own. Results show that our online compression strategy is capable of outperforming the baseline even for conditions when the number of features per key-frame used for mapping is four times less.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4509-4514
Number of pages6
ISBN (Electronic)9781509046331
ISBN (Print)9781509046348
DOIs
Publication statusPublished - 24 Jul 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
CountrySingapore
CitySingapore
Period29/05/173/06/17

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