The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 9–14, 2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 9–14, 2022
30 May 2022
30 May 2022


A. Azimi1, A. Hosseininaveh1, and F. Remondino2 A. Azimi et al.
  • 1Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, Toosi University of Technology, K. N., Tehran, Iran
  • 23D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: Visual Odometry, Visual SLAM, Visual-Inertial Systems, IMU, Geometric Key-Frame Selection

Abstract. Given the importance of key-frame selection in determining the positioning accuracy of Simultaneous Localization And Mapping (SLAM) and Odometry algorithms, and the urgent need in this field for a flexible key-frame selection algorithm, this paper proposes a novel and geometric method for key-frame selection built on top of ORB-SLAM3. It takes a key-frame in a completely robust and flexible way regardless of the environment, data and scene conditions, and according to the physics and geometry of the environment. In the proposed method, the camera sensor and IMU take key-frames simultaneously and in parallel. While selecting a key-frame, an adaptive threshold first decides whether the geometric condition of the frame is appropriate based on the degree of change in the orientation of the point visibility vector from the last key-frame to the current frame. Then the quality of the frame is evaluated by examining the distribution of points inside the frame by a balance criterion. A new key-frame will be created if both conditions provide a positive answer. In addition, if the IMU sensor detects large changes in acceleration, a key-frame independently chosen. The proposed method is evaluated qualitatively and quantitatively on the EuRoC dataset by comparing the algorithm trajectory to a reference trajectory and usig the Absolute Trajectory Error (ATE) and the processing time as metrics. The evaluation results indicate a 26% improvement in the positioning of the algorithm although it has a 9% increase in the processing time due to its geometric key-frame selection process.