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.
CITATION STYLE
Webb, A. M., Brown, G., & Luján, M. (2019). ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11649 LNAI, pp. 221–235). Springer Verlag. https://doi.org/10.1007/978-3-030-23807-0_19
Mendeley helps you to discover research relevant for your work.