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.

过去几年开发了关键帧选择方法，以降低视觉里程计 (VO) 和视觉同时定位和映射 (VSLAM) 算法中帧处理的复杂性。关键帧通过稀疏帧来帮助提高算法的性能，同时保持其准确性和鲁棒性。与当前依赖许多启发式阈值来决定应该选择哪个关键帧的选择方法不同，本文提出了一种基于 ORB-SLAM3 的基于摄影测量的关键帧选择方法。所提出的算法，命名为摄影测量关键帧选择（PKS），用摄影测量原理代替静态启发式阈值，确保算法的鲁棒性和更好的点云质量。基于自适应阈值和重心平衡 (ECOG) 标准以及惯性测量单元 (IMU) 观察来选择关键帧。为了评估提出的 PKS 方法，使用了欧洲机器人挑战赛 (EuRoC) 和内部数据集。通过比较单惯性和立体惯性模式下的轨迹、点云质量和完整性以及绝对轨迹误差 (ATE) 进行定量和定性评估。此外，对于生成的密集点云，进行了广泛的评估，包括平面拟合误差、模型变形、模型对齐误差以及模型密度和质量。结果表明，在不使用启发式阈值的情况下，所提出的算法在立体惯性模式下将 ORB-SLAM3 定位精度提高了 18%，在单惯性模式下提高了 20%，以及产生高达 50% 的更完整和准确的点云。所提出方法的开源代码可在 https://github.com/arashazimi0032/PKS 获得。