Abstract: A method of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

Abstract: A method and system for navigating a mobile system and corresponding mobile system is disclosed, in particular for autonomous mobile systems such as robots, for example lawn mowers or even smartphones. The mobile device includes at least one sensor, an electronic control unit, and an output unit. The method includes acquiring sensor data on an environment of the mobile device, calculating a gradient of a difference of a target environmental representation and a current environmental representation, and determining a movement direction to reach a target position corresponding to the target environmental representation based on the estimated gradient. The determined movement direction for navigating the mobile device is output, for example to a steering system of the mobile device or to a display. The method can include generating an environmental representation by performing unsupervised learning from the acquired sensor data.

Abstract: A method of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

Abstract: A method and system for navigating a mobile system and corresponding mobile system is disclosed, in particular for autonomous mobile systems such as robots, for example lawn mowers or even smartphones. The mobile device includes at least one sensor, an electronic control unit, and an output unit. The method includes acquiring sensor data on an environment of the mobile device, calculating a gradient of a difference of a target environmental representation and a current environmental representation, and determining a movement direction to reach a target position corresponding to the target environmental representation based on the estimated gradient. The determined movement direction for navigating the mobile device is output, for example to a steering system of the mobile device or to a display. The method can include generating an environmental representation by performing unsupervised learning from the acquired sensor data.

Abstract: Provided is a method for processing data from a mass spectrum generated from a sample, which method comprises: (a) selecting a first peak in the mass spectrum; (b) selecting a first monoisotopic reference ion having a first charge state, which first reference ion could contribute to the first peak; (c) for one or more other isotopic forms of the first reference ion determining one or more further expected peaks in the mass spectrum; (d) comparing one or more of the determined further expected peaks with the mass spectrum to determine whether there are one or more peaks present in the spectrum that match the one or more determined further expected peaks; (e) if one or more of the determined further expected peaks match one or more of the peaks in the mass spectrum, designating the first peak as a data peak, and optionally designating the one or more peaks present in the spectrum that match the one or more determined further expected peaks as data peaks; (f) if the determined further expected peaks do not match