4. Unscented RGB-D SLAM in Indoor Environment

The research considers the implementation of simultaneous localization and mapping algorithm based on the FastSLAM technique and specific problems that are typical for the RGB-D sensor-based solutions. An improvement of the classical FastSLAM algorithm has been obtained by replacing the method of landmarks’ observations filtering with unscented Kalman filters. Instead of linearizing, the nonlinear models through the first order Taylor series expansion at the mean of the landmark state were applied. The proposed algorithm computes a more accurate mean and uncertainty of the landmarks, which are moving nonlinearly. Various data preprocessing issues are discussed, such as the method of calibration of Kinect-like cameras, depth map restoration using a modified interpolation technique, and filtering the noise in the RGB images for more accurate detection of key features. Additionally, the chapter presents an improved resampling algorithm for the particle filtering through the adaptive thresholding based on the data of the effective particle number evolution. The proposed algorithm runs in real time and shows good accuracy and robustness in comparison with other modern SLAM systems using all the advantages and disadvantages of the RGB-D sensors.