Modeling movable objects improves localization in dynamic environments

Most state-of-the-art robotic maps assume a static world; therefore, dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving, i.e., semi-static objects, which are usually recorded in the map and treated as static objects, violating the static world assumption and causing errors in the localization. This paper presents a method for modeling moving and movable objects to match the map and measurements consistently. This reduces the error resulting from inconsistent categorization and treatment of non-static measurements. A semantic segmentation network is used to categorize the measurements into static and semi-static classes, and a background subtraction-based filtering method is used to remove dynamic measurements. Experimental comparison against a state-of-the-art baseline solution using real-world data from the Oxford Radar RobotCar data set shows that consistent assumptions over dynamics increase localization accuracy.