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Found 5 papers, 2 papers with code
MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
The dataset is photo-realistic and provides both the localization and the map ground truth data.
Map-merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
Simultaneous localization and mapping, especially the one relying solely on video data (vSLAM), is a challenging problem that has been extensively studied in robotics and computer vision.
Real-time Vision-based Depth Reconstruction with NVidia Jetson
Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution.
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision.
Original Loop-closure Detection Algorithm for Monocular vSLAM
Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays.
Loop Closure Detection
Simultaneous Localization and Mapping
