Sparser Relative Bundle Adjustment (SRBA)-(V-SLAM)

Bundle adjustment is almost always used as the last step of every feature-based 3D Reconstruction algorithm. It amounts to an optimization problem on the 3D structure and viewing parameters (i.e., camera pose and possibly intrinsic calibration and radial distortion), to obtain a reconstruction which is optimal under certain assumptions regarding the noise pertaining to the observed

Image features: If the image error is zero-mean Gaussian, then bundle adjustment is the Maximum Likelihood Estimator(MLE)

Its name refers to the bundles of light rays originating from each 3D feature and converging on each camera's optical center, which are adjusted optimally with respect to both the structure and viewing parameters (similarity in meaning to categorical bundle seems a pure coincidence). Bundle adjustment was originally conceived in the field of photogrammatery

Bundle Adjustment is the name given to one solution to visual SLAM based on maximum-likelihood estimation (MLE) over the space of map features and camera poses. However, it is by no way limited to visual maps, since the same technique is also applicable to maps of pose constraints (graph-SLAM) or any other kind of feature maps not relying on visual information

Links: https://mrpt.github.io/srba/