Large-scale direct SLAM for omnidirectional cameras

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WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, methodology): “As a comparison, to give the reader an understanding of how different models behave, Figure 3 shows the mapping function r(θ) for five different projection models, which are Polynomial, Rectilinear, Stereographic, UCM and eUCM.”
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras (2021, background): “For example, ORB-SLAM3 [5] supports monocular, stereo, RGB-D, and IMU sensors, and modern systems can support a variety of camera models [5, 27, 42, 6].”
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (2017, background): “Since then, the semi-dense direct VO/SLAM has been further extended by support for omnidirectional cameras [1] and tightly coupling with IMU [28].”
Direct Sparse Odometry (2016, background): “Note that analogously to [2], our approach can be extended to other (invertible) camera models, although this does increase computational demands.”
Visual SLAM algorithms: a survey from 2010 to 2016 (2017, methodology): “LSD-SLAM is extended to stereo cameras and omni-directional cameras [49, 50].”
Visual-Inertial Navigation: A Concise Review (2019, background): “This work was later extended from a monocular sensor to stereo and omnidirectional cameras for improved accuracy [116, 117].”