PKS: A photogrammetric key-frame selection method for visual-inertial systems built on ORB-SLAM3

Abstract

Key-frame selection methods were developed in the past years to reduce the complexity of frame processing in visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) algorithms. Key-frames help increasing algorithm's performances by sparsifying frames while maintaining its accuracy and robustness. Unlike current selection methods that rely on many heuristic thresholds to decide which key-frame should be selected, this paper proposes a photogrammetric-based key-frame selection method built upon ORB-SLAM3. The proposed algorithm, named Photogrammetric Key-frame Selection (PKS), replaces static heuristic thresholds with photogrammetric principles, ensuring algorithm’s robustness and better point cloud quality. A key-frame is chosen based on adaptive thresholds and the Equilibrium Of Center Of Gravity (ECOG) criteria as well as Inertial Measurement Unit (IMU) observations. To evaluate the proposed PKS method, the European Robotics Challenge (EuRoC) and an in-house datasets are used. Quantitative and qualitative evaluations are made by comparing trajectories, point clouds quality and completeness and Absolute Trajectory Error (ATE) in mono-inertial and stereo-inertial modes. Moreover, for the generated dense point clouds, extensive evaluations, including plane-fitting error, model deformation, model alignment error, and model density and quality, are performed. The results show that the proposed algorithm improves ORB-SLAM3 positioning accuracy by 18% in stereo-inertial mode and 20% in mono-inertial mode without the use of heuristic thresholds, as well as producing a more complete and accurate point cloud up to 50%. The open-source code of the presented method is available at https://github.com/arashazimi0032/PKS.