Consequently, it will be too pricey in terms of computational memory to compute the posterior under this first approach. It is obvious that maps have high dimensionality. However, keep in mind that the robot controls will be included later in SLAM to estimate the robot’s trajectory. In estimating the map, we’ll exclude the controls u since the robot path is provided to us from SLAM. With this function, we can compute the posterior over the map given all the measurements up to time t and all the poses up to time t represented by the robot trajectory. The Mapping with Known Poses problem can be represented with P(m∣z1:t,x1:t) function. Going back to the graphical model of mapping with known poses, the goal is to implement a mapping algorithm and estimate the map given noisy measurements and assuming known poses. In the SLAM, the robot pose and environment map is generated while in the mapping, the poses are taken filtered and assumed as known. In robotics, mapping takes place after SLAM. This is a pseudo C++ package for doing mapping based on the occupancy grid
