SLAM

Abstract Robots have become present in our everyday life. Robotic vacuum cleaners and lawnmowers take care of our homes, self-driving cars provide personal mobility in radically new ways, collaborative production assistants work side-by-side with humans in modern factories, and last-mile delivery platforms transport goods to their destination in intralogistics and urban spaces. These and many other applications have in common the need for an internal representation of the surrounding environment and require knowing the pose of the robot within this environment. In view of this, researchers during last decades invested substantial effort in finding solutions to this problem, converging in a field named SLAM. In last years, the evolution of this field brought major breakthroughs that lead to structural changes in the core algorithms and the way the SLAM problem was framed. This dynamic evolution made it difficult to find a unified SLAM formulation that generalizes the different research..