RGB-D Dense Visual Odometry through Pixel Level Segmentation in Dynamic Environments

Estimating a camera pose in dynamic environments is one of the challenging problems in Visual Odometry. We propose an RGB-D Dense Visual Odometry (Dense-VO) system which uses preprocessed images that passed the Convolutional Neural Network (CNN). The algorithm adopts the CNN that tracks the designated dynamic object. The tracked dynamic object is excluded when the Dense-Vo estimates the camera motion by minimizing photometric error between consecutive images. The system was tested in two datasets which includes a dynamic object. The proposed approach containing the preprocessing procedure estimates the camera trajectory with less drift in a dynamic environment.