ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language | English |
|---|---|
| Title of host publication | Towards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings |
| Editors | Kaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang |
| Publisher | Springer Nature |
| Pages | 221-235 |
| Number of pages | 15 |
| ISBN (Print) | 9783030238063 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 20th Annual Conference on Towards Autonomous Robotic Systems - London, United Kingdom Event duration: 3 Jul 2019 → 5 Jul 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11649 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 20th Annual Conference on Towards Autonomous Robotic Systems |
|---|---|
| Abbreviated title | TAROS 2019 |
| Country | United Kingdom |
| City | London |
| Period | 3/07/19 → 5/07/19 |