Abstract: Map validation method, including the steps of: receiving (S10) sensor data of an at least semi-autonomous robot (F) for depicting at least one detected element (S3, S4, S5), wherein the at least one detected element (S3, S4, S5) represents an environmental element of the at least semi-autonomous robot (F) as detected by an environmental sensor of the at least semi-autonomous robot (F); receiving (S20) map data (Dk) depicting a map with at least one map element (A3, A4, A5), wherein the at least one map element (A3, A4, A5) represents an environment element of the at least semi-autonomous robot (F) as plotted on a predetermined map; receiving (S30) localization data (Dl), the localization data (Dl) indicating a position of the at least semi-autonomous robot (F) on the map; determining (S40) a data uncertainty, wherein the data uncertainty comprises a sensor data uncertainty, a map data uncertainty, and/or a localization data uncertainty; initializing (S50) an existence probability (P) for the at least one m

Abstract: A method for the semantic segmentation of an image having a two-dimensional arrangement of pixels comprises the steps of segmenting at least a part of the image into superpixels, determining image descriptors for the superpixels, wherein each image descriptor comprises a plurality of image features, feeding the image descriptors of the superpixels to a convolutional network and labeling the pixels of the image according to semantic categories by means of the convolutional network, wherein the superpixels are assigned to corresponding positions of a regular grid structure extending across the image and the image descriptors are fed to the convolutional network based on the assignment.