LSU_AIM2024

This zip file contains a subset of the image sequences and all YOLOv5 neural networks used in the "Solving Short-Term Relocalization Problems in Monocular Keyframe Visual SLAM Using Spatial and Semantic Data" paper. This paper was presented in 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) held in Boston, MA, USA.

The proposed framework, along with links to the preprint paper and presentation slides is made publicly available in this GitHub repository: https://github.com/RKinDLab/ros2_psd_pcb_reloc

Note that, along with our in-house UGV dataset, this zip file, for ease of demonstration, also contains the following open-source image sequences. The name of these sequences along with appropriate citation are tabulated below

1. MH01, V201: M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. Achtelik and R. Siegwart, The EuRoC micro aerial vehicle datasets, International Journal of Robotic Research, DOI: 10.1177/0278364915620033, early 2016.
   
   
2. FR2PS1:  Sturm, Jürgen et al. “A benchmark for the evaluation of RGB-D SLAM systems.” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (2012): 573-580.