Monocular simultaneous localization and mapping (SLAM) attracted much attention in the mobile-robotics domain over the past decades along with the advancements of small-format, consumer-grade digital cameras. This is especially the case for micro air vehicles (MAV) due to their payload and power limitations. The quality of global 3D reconstruction by SLAM solutions is a critical factor in occupancy-grid mapping, obstacle avoidance, and map representation. Although several benchmarks have been created in the past to evaluate the quality of vision-based localization and trajectory-estimation, the quality of mapping products has been rarely studied. This paper evaluates the quality of three state-of-the-art open-source monocular SLAM solutions including LSD-SLAM, ORB-SLAM, and LDSO in terms of the geometric accuracy of the global mapping. Since there is no ground-truth information of the testing environment in existing visual SLAM benchmark datasets (e.g., EuRoC, TUM, and KITTI), an evaluation dataset using a quadcopter and a terrestrial laser scanner is created in this work. The dataset is composed of the image data extracted from the recorded videos by flying a drone in the test environment and the high-fidelity point clouds of the test area acquired by a terrestrial laser scanner as the ground truth reference. The mapping quality evaluation of the three SLAM algorithms was mainly conducted on geometric accuracy comparisons by calculating the deviation distance between each SLAM-derived point clouds and the laser-scanned reference. The mapping quality was also discussed with respect to their noise levels as well as further applications.
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