COLMAP-SLAM: A FRAMEWORK FOR VISUAL ODOMETRY

Abstract

SLAM technology is more and more integrated with other sensors for indoor and outdoor seamless navigation. This research topic is very active in particular on image matching with deep learning local features, keyframe selection approaches, or tests on new IMU and GNSS solutions. Integrating and testing new methodologies on other widely used SLAM implementations, such as ORB-SLAM, can be not a trivial task. Therefore, we propose an extension of COLMAP to be used in real-time as a feature-based Visual-SLAM that can be also coupled with other sensors. COLMAP has been chosen due to its modularity and the large community that assures the continuity of the repository. The paper presents a pipeline mainly thought for real-time evaluation of learning-based tie points and new SLAM features, that works with both monocular, stereo and multi-camera systems. It is also shown an example of keyframe selection algorithm based on deep learning local features, and a simple example of IMU integration. The code is available on the GitHub repository https://github.com/3DOM-FBK/COLMAP_SLAM.