## 2 Materials and Methods
### Selected Algorithms
The global depth map is rendered as a pose graph comprising keyframes represented as vertices that present feature 3D similarity transformations as edges, adding environment scaling ability and allowing the accumulated drift to be detected and corrected. Furthermore, the LSD-SLAM uses an appearance-based loop detection algorithm called FAB-MAP [58], introducing prominent loop closure candidates that extract their features without reusing any additional visual odometry information. The LSD-SLAM is publicly available in [59] and was implemented in Ubuntu 18.04 in its ROS version. 4. **DSO**. The DSO [21] is widely known as the direct methods' gold standard due to the impressive reconstruction and odometry results that it has achieved, inspiring other implementations and new proposals. The DSO works directly on the pixel intensity information but applies a point selection strategy to reduce the amount of information to be processed efficiently, continuously optimizing the photometric error applied to the last N-frames while optimizing the complete likelihood for the parameters involved in the model, including poses, intrinsics, extrinsics, and inverse depths, executing a windowed sparse bundle adjustment. The DSO is publicly available for implementation in [60]; its code runs entirely in C++, using minor requirements like Suitesparse, Eigen3, and Pangolin. * 5. **SVO. ** We selected the most commonly known method, SVO [12], for the hybrid classification. The SVO efficiently combines the advantages of the direct and indirect approaches by using the feature correspondences obtained on the direct motion estimation for tracking and mapping. This procedure considerably reduces the number of required features and is only executed when a new keyframe is selected to insert new points in the map. First, camera motion is estimated by a sparse model-based image alignment algorithm, where sparse point features are used to estimate the feature correspondences. Next, this information is used to minimize the photometric error. Then the reprojected points, pose, and structure are refined by minimizing the reprojection error. The SVO is publicly available in [61] for testing and implementation running on C++ or ROS. Modern operating systems might find issues during implementation; so, Ubuntu 16.04 and ROS kinetic were used. * 6. **LDSO. ** As an additional sparse direct system, the LDSO [30] was selected as an update of the DSO algorithm that includes loop-closure capabilities. The LDSO enables the DSO framework to detect the loop closure by ensuring point repeatability using corner features to detect loop candidates. For this purpose, the depth estimates for point features allow the algorithm to compute the \(Sim(3)\) constraints, to be combined with the pose-only bundle adjustment and point cloud alignment and fused with the relative pose DSO covisibility graph, sliding the window optimization stage. This way, the LDSO adds the loop closure to the DSO system, including a loop closure module based on a global pose graph optimization working over the last five to seven keyframes' sliding window. The LDSO was made publicly available in [62], and for this comparison, it was implemented in Ubuntu 18.04 along with OpenCV 2.4.3, Sophus, DBoW3, and g2o. * 7. **DSM. ** Another sparse-direct method we were interested in testing was the DSM [31], another update made to the DSO to create a complete SLAM system. The DSM aimed to include scene reobservation information to enhance the precision and reduce the drift and inconsistencies. In contrast to the LDSO, which considers a sparse set of reobservations, the DSM builds a persistent map allowing the algorithm to reuse existing information by a photometric formulation. The DSM uses local map covisibility window criteria to detect the active keyframes reobserving the same region, a coarse-to-fine strategy to process that point reobservation information and a robust nonlinear photometric bundle adjustment technique based on the photometric error for outlier detection.