Abstract: Training a pose estimator. The pose estimator may receive as input a sensor measurement representing an object and to produce a pose of the object as output. Training the pose estimator may include applying multiple trained initial pose estimators to a pool of sensor measurements to obtain multiple estimated poses for a sensor measurement. A further pose estimator may be trained on multiple training data sets using at least part of an autoencoder trained on the multiple estimated poses to map a pose from a first pose format to a second pose format.

Abstract: A method for predicting at least one future velocity vector and/or a future pose of a pedestrian in an area of prediction. A map of a surrounding environment of the pedestrian and current velocity vectors of other pedestrians in the area of prediction are taken into account in the prediction.

Abstract: A method for determining objects in an environment with the aid of SLAM and a mobile device in the environment, which has at least one sensor for acquiring object and/or environment information. The method includes: providing sensor data, carrying out an object detection in order to obtain first object datasets for detected objects; carrying out object tracking for a new SLAM dataset, including allocating objects detected with the aid of the object detection to real objects in order to obtain second object datasets or real objects to be considered in the SLAM graph.

Abstract: A computer-implemented method for planning a motion path for multiple agents. The method includes: performing a conflict-based motion planning for the multiple agents, wherein conflict-free motion paths for each of the agents are determined depending on movement costs, determining the poses and velocities of one or more individual objects and one or more groups of objects; calculating the movement costs depending on interaction costs of each of the agents with the one or more objects and/or the one or more groups of objects.

Abstract: A method for predicting at least one future velocity vector and/or a future pose of a pedestrian in an area of prediction. A map of a surrounding environment of the pedestrian and current velocity vectors of other pedestrians in the area of prediction are taken into account in the prediction.