Improvement of Visual Odometry Based on Robust Feature Extraction Considering Semantics

Visual odometry is a key technology for an autonomous robot to accurately determine its locations on a map accurately if a camera is the main external sensor. If IMU is available, the scale information can also be estimated by combining visual and IMU information, which is called Visual Inertial Odometry (VIO). This research attempts to modify VINS-Mono, a widely used VIO method, to improve the estimation accuracy in symbiotic environments with people in outdoor scenes, where estimation accuracy becomes worsens according to the dynamic obstacles in images. The proposed method replaces a method for feature point extraction in VINS-Mono and adds a process to limit the area for the feature point extraction using semantic information obtained from segmentation results. Experimental results using datasets created from actual environments demonstrate that SuperPoint showed the best accuracy for most scenes and that area limitation improved estimation accuracy when many dynamic obstacles were included in the input images.