Abstract
We present a novel approach for autonomous location estimation and navigation in indoor environments using range images and prior scene knowledge from a GIS database (CityGML). What makes this task challenging is the arbitrary relative spatial relation between GIS and Time-of-Flight (ToF) range camera further complicated by a markerless configuration. We propose to estimate the camera's pose solely based on matching of GIS objects and their detected location in image sequences. We develop a coarse-to-fine matching strategy that is able to match point clouds without any initial parameters. Experiments with a state-of-the-art ToF point cloud show that our proposed method delivers an absolute camera position with decimeter accuracy, which is sufficient for many real-world applications (e.g., collision avoidance).
Highlights
Even though indoor positioning is a comparatively new research topic, the development of indoor positioning techniques has become a major research field
The measuring principle of the MESA® ToF camera SwissRanger 4000, schematically shown in Figure 5, is based on the phase shift between light emitted from a light source and the reflected light received at a sensor using Complementary Metal Oxide Semiconductor technology (CMOS/CCD) [31]
To reduce the signal to noise ratio (SNR) a mean point cloud was averaged over 100 measurements
Summary
Even though indoor positioning is a comparatively new research topic, the development of indoor positioning techniques has become a major research field. Laser scanners, measuring each point sequentially, triangulation methods (e.g., stereo-vision and photogrammetry), and interferometry are commonly used for optical based indoor positioning Drawbacks of these techniques include time-consuming data acquisition due to the sequential scanning process of terrestrial laser scanners, challenging stereo image analysis for stereo camera systems or visual odometry [3], and limited depth range for interferometric methods [4]. Since relative orientations of objects and absolute position are known therein, we can use this information to pose our measuring device once newly acquired point clouds are accurately matched to the model. Such models have become widely available because various disciplines like.
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