Carsten Isert

Self-contained indoor positioning on off-the-shelf mobile devices

We introduce a novel self-contained seamless positioning solution for indoor and outdoor environments, well suited for and designed to be operated on off-the-shelf mobile phones. Position information is deduced from a combination of GNSS where available, combined with Pedestrian Dead Reckoning (PDR) utilizing inertial measurements and context-aware activity based map matching. The proposed system heavily exploits different types of human movement, such as walking, running, ascending or descending stairs, to improve the employed positioning model. In remaining independent from any external infrastructure, accurate localization is also possible in environments, where the installation and maintenance of such infrastructure does not make sense or is simply not affordable - as for example in a parking garage to guide a user to the next exit or back to his car. Concerning this particular use case we have also implemented an interface for the synchronization of location information between the mobile positioning solution and the car.

Improved vehicle positioning for indoor navigation in parking garages through commercially available maps

We present a method that allows precise vehicle positioning inside buildings like parking garages. It works independently of GNSS and instead uses dead reckoning (DR) and a digital map of the parking garage. No additional hardware or infrastructure is needed, since the used inertial sensors and odometry are available in current premium series vehicles. Only additional map data for parking garages is required using existing format specifications. This data can be gathered with current technology available at map companies or from building plans. The position can be determined approximately to a single parking spot and be used for further indoor navigation on a mobile phone. Concerning this particular use case we have also implemented an interface for synchronization of location information between the mobile positioning solution and the car. When leaving the building navigation can start immediately to guide the user to the right exit.

Camera based step detection on mobile phones

This paper presents an extension to an indoor positioning system leveraging the camera which is built into current mobile phones to detect steps. Current indoor positioning systems on mobile phones based on pedestrian dead reckoning (PDR) often rely on step length and step frequency estimation. Usually the accelerometer is used to determine these values. Depending on the attitude of the device or the smoothness of the movement sometimes steps are not correctly detected. We propose to use the camera as additional sensor element to detect the forward section of both feet appear and disappear in the camera image and detect the steps based on this information. A comparison to an accelerometer based approach is provided.

Online Active Calibration for a Multi-LRF System

Multi-LRF(Laser Range Finder) systems have been broadly utilized in sensor fusion for automobile. In order to convert multiple LRF data into a unified coordinate system, we have to obtain the rigid transformation among multi-LRF. In this paper, we propose a new algorithm for online extrinsic calibration of multi-LRFs by observing a planar checkerboard pattern and solving for transformation between the views of a planar checkerboard from a camera and multi-LRF. Existing LRF calibration is achieved by freely moving a checkerboard pattern and conducting much offline optimization. Compared with traditional algorithm, the advantages of our approach are twofold. Firstly, adopting the noise of images and LRF depth readings, we can exactly calculate the exact position and pose of the checkerboard that can largely reduce the transformation error. Secondly, the complete calibration process is online, which means the exact position and pose of the checkerboard can be obtained in real-time and manipulated by robotic arm. In the end, our calibration approach is validated through real experiments that show the superiority with respect to the state-of-art methods.

Car2X Technology-based “Green Light on Demand” System

Car2X technology helps obtain information about individual vehicles, which acts as an accurate data resource for determining traffic status from the viewpoint of a traffic signal controller designing phase timing. Based on Car2X technology, a prototype system for “Green Light on Demand,” which means adapting traffic signal automatically for privileged vehicles if necessary, is developed. Two types of passing algorithms for privileged vehicles at a single intersection are proposed. The first algorithm was tested by conducting a field experiment, and the result demonstrates that the system can reduce the number of stops for privileged vehicles. The second algorithm was tested in a simulation experiment, the results of which prove that the signal control algorithm can reduce the travel time and number of stops for privileged vehicles, while not having any significant effect on normal vehicles.

Pre- and Postdrive Predictions

The prediction of a destination is of great use for advanced driver assistance systems (ADAS). However, clustering and predicting locations based only on latitude/longitude coordinates from cars lacks semantic information about locations and can be imprecise, especially once the car is parked. The functionality of these predictive systems can be greatly enhanced when the data used for predictions is extended beyond the vehicle and when predictions can already be made before entering the car. Therefore, we propose to include location-based services running on the driver’s smartphone and provide the prediction via a web service. The inclusion of explicit check-ins is used to extract semantic meaning of places and to improve predictions beyond the parking spot of the vehicle. We implemented a prototype, which combines data from foursquare and Google Latitude and enables predicting not only the locations but also the most probable times when a user will arrive at a certain place and when that user will leave this place. This information enables preconditioning and optimization of charging strategies for electric vehicles and can improve recommendation systems. This paper demonstrates the overall system architecture and explains the prototype implementation.