Three-dimensional scene reconstruction and Kalman filter

Simultaneous Localization and Mapping (SLAM) is the task of reconstructing an environmental model passed using onboard sensors and at the same time maintaining an estimate of the mobile sensor location within the model. One of the known approaches to the SLAM problem is the Kalman filter. The Kalman filter efficiency is based on the fact that it contains a fully correlated posterior over feature maps and mobile sensor poses. The important element of the SLAM problem is the reconstruction of the environmental 3D scene. In this paper, we propose an algorithm to restore the 3D scene using consistent condition and a modified version of the Kalman filter. The reconstruction algorithm is noniterative. Computer simulation results are provided to illustrate the performance of the proposed method.