ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recent work has integrated semantics into the 3D scene models produced by visual SLAM systems. Though these systems operate close to real time, there is lacking a study of the ways to achieve real-time performance by trading off between semantic model accuracy and computational requirements. ORB-SLAM2 provides good scene accuracy and real-time processing while not requiring GPUs [1]. Following a ‘single view’ approach of overlaying a dense semantic map over the sparse SLAM scene model, we explore a method for automatically tuning the parameters of the system such that it operates in real time while maximizing prediction accuracy and map density.

Bibliographical metadata

Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings
EditorsKaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
PublisherSpringer Nature
Pages221-235
Number of pages15
ISBN (Print)9783030238063
DOIs
Publication statusPublished - 2019
Event20th Annual Conference on Towards Autonomous Robotic Systems - London, United Kingdom
Event duration: 3 Jul 20195 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11649 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Annual Conference on Towards Autonomous Robotic Systems
Abbreviated titleTAROS 2019
CountryUnited Kingdom
CityLondon
Period3/07/195/07/19

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