Statistical indoor localization using fusion of depth-images and step detection

This paper presents a method for indoor localization of humans. Our new approach combines an imaged-based position estimation with a given step and turn detection. Estimating the position of an object is not only a question of accuracy, it is also a question of performance and time required. Our approach uses a fusion of an uncalibrated depth-sensor with a smartphone's accelerometer and gyroscope. Both components do not rely on time-consuming practices like fingerprinting and calibration techniques. This approach allows for a real time-tracking by using well-known methods of recursive density propagation and particle filtering. Unlike other image-based methods in autonomous robotics, the depth sensors are mounted at a fixed position. Therefore we will show how our new statistical sensor model covers the four main conditions of an image-based approach: a person is either inside or outside the field of view and is detected or not detected by the depth sensor. This involves the possibility to estimate the position of multiple persons at each point in time. Finally, the experimental results show how the integration of an image-based approach increases the accuracy of the localization estimation and counteracts the increasing error of the given step and turn detection.