3D Indoor Depth Mapping Using SIFT Feature Based ICP Registration

Abstract

3D mapping is one of the challenging research areas of Computer Vision and Robotics. Mappings are often done with mobile robots. One of the best approach is the use of SLAM (Simultaneous Localization and Mapping) where a robot roams around and builds the map and also localizes its position on the currently built map. The robot needs the sense of depth of the environment and often equipped with depth sensor. It also needs Odometry data from the accelerometer or encoder set on the wheels. There are some other solutions where the map and localization is based on the features extracted and builds the map based on the depth images and use of loop closure by adjusting the error in perception. In this thesis work we used feature based mapping which does not need any Odometry data and the map can be built based on RGB image and Depth data. We used Kinect, a very popular depth sensor which is effective in this process under certain constraints. Here in our work we gathered the RGB and Depth data simultaneously and then chose suitable frames and found key features with SIFT algorithm. The extracted features in the RGB images has corresponding 3D co-ordinates. The 3d co-ordinates have been found through intensive experiments done on calibrating the Kinect sensor and finding the best fit value of parameters to give more accurate 2D spatial points corresponding to each frame captured. The matched 3D points were then applied in Iterative closest Point (ICP) algorithm as initial points to merge the two depth images. When two images are merged they were saved in separated storages and another frame is extracted and registered with the previous ones. In this methodology multiple frames were stitched in 3D spatial co-ordinates. After forming the indoor map the map was evaluated with respect to the lengths of the objects formed in the map and the shape of the map along different 2D planes by comparing their area. Different version of ICP give different result in time complexity and accuracy. It was found IRLS (Iteratively Reweighted Least Square) ICP gave better results. The methodology we proposed can be implemented at faster time and fewer constraints and mapping do not depend on the movement noise of the robot. So the whole process is simpler and robust and can be used in indoor mapping, object detection and security purposes.