Proc. SPIE. 10332, Videometrics, Range Imaging, and Applications XIV
KEYWORDS: Mobile devices, Optical filters, Data modeling, Cameras, Sensors, Particle filters, Magnetic sensors, Stochastic processes, Systems modeling, Global Positioning System, Process modeling, Instrument modeling
Most of the applications with mobile devices require self-localization of the devices. GPS cannot be used in indoor environment, the positions of mobile devices are estimated autonomously by using IMU. Since the self-localization is based on IMU of low accuracy, and then the self-localization in indoor environment is still challenging. The selflocalization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method without GPS in indoor environment by integrating sensors, such as IMU and cameras, on mobile devices simultaneously. The proposed method consists of observations, forecasting and filtering. The position and velocity of the mobile device are defined as a state vector. In the self-localization, observations correspond to observation data from IMU and camera (observation vector), forecasting to mobile device moving model (system model) and filtering to tracking method by inertial surveying and coplanarity condition and inverse depth model (observation model). Positions of a mobile device being tracked are estimated by system model (forecasting step), which are assumed as linearly moving model. Then estimated positions are optimized referring to the new observation data based on likelihood (filtering step). The optimization at filtering step corresponds to estimation of the maximum a posterior probability. Particle filter are utilized for the calculation through forecasting and filtering steps. The proposed method is applied to data acquired by mobile devices in indoor environment. Through the experiments, the high performance of the method is confirmed.
Recently, various kinds of vector data have been widely used. Images as raster data also became popular, and then applications using the vector data and images simultaneously attract more interests. Such applications require registration of those data in a same coordinates system. This paper proposes an orientation method combining the vector data with the images based on bundle adjustment. Since the vector data can be regarded as constraint condition, the bundle adjustment is extended to constrained non-linear optimization method. The constraint conditions are coincidence between lines extracted from images and the corresponding ones of vector data. For formulation, a representative point is set as midpoint of a projected line of vector data on the image. By using the representative points, the coincidence condition is expressed as distance the point and the lines extracted from the image. According to the conditions, the proposed method is formulated as Lagrange's method of undetermined multipliers. The proposed method is applied to synthetic and real data (compared with laser scanner data). The experiments with both synthetic and real data show that the proposed method is more accurate to errors caused by low accuracy of coordinates of feature points than a method without constraint conditions. According to the experiments, the significance of the proposed method is confirmed.
Recently microscopic understanding of individual pedestrian behavior in public space is becoming significant. Observation data from diverse sensors have increased. Meanwhile some simulation models of human behavior have made progress. This paper proposes a method of multiple human tracking under the complex situations by integrating the various observation data and the simulation. The key concept is that the multiple human tracking can be regarded as stochastic process modeling. A data assimilation technique is employed as the stochastic process modeling. The data assimilation technique consists of observations, forecasting and filtering. For the modeling, a state vector is defined as an ellipsoid and its coordinates, which are human positions and shapes. An observation vector is also defined as observations from stereo video camera, namely color and range information. Then a system model which represents dynamics of the state vectors is formulated by using discrete choice model. The discrete choice model decides the next step of each pedestrian stochastically and deals with interaction between pedestrians. An observation model is also formulated for the filtering step. The likelihood of color is modeled based on color histogram matching, and one of range is calculated by comparing between the ellipsoidal model and observed 3D data. The proposed method is applied to the data acquired at the ticket gate of a station and the high performance of the method is confirmed. We compare the results with other models and show the advantage of integrating the behavior model to the tracking method.
Conference Committee Involvement (2)
Videometrics, Range Imaging, and Applications
26 June 2017 | Munich, Germany
Videometrics, Range Imaging, and Applications XIII