###### Abstract  
The problem of data enrichment to train visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train a model, and more data with many different contextual and conditional conditions will create a more accurate visual SLAM and VO construction model. In this paper, we introduce the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs. It was collected from the corridors of three interconnected buildings comprising a length of about 230 m. The ground-truth data of the TQU-SLAM benchmark dataset were prepared manually, including 6-DOF camera poses, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world. We also tested the TQU-SLAM benchmark dataset using the PySLAM framework with traditional features such as SHIL_TOMASI, SIFT, SURF, ORB2, AKAZE, KAZE, and BRISK and features extracted from DL such as VGG, DPVO, and TartanVO. The camera pose estimation results are evaluated, and we show that the ORB2 features have the best results (\(Err_{d}=5.74\) mm), while the ratio of the number of frames with detected keypoints of the SHIL_TOMASI feature is the best (\(r_{d}=98.97\%\)). At the same time, we also present and analyze the challenges of the TQU-SLAM benchmark dataset for building visual SLAM and VO systems. T 2024 16 174 10.3390/16/050174 10.3390/16/174 10.3390/16/050174 10. Based on feature-based methods, the feature-detection step comprises the process of feature extraction, such as SIFT [4], SURF [5], ORB [6], and BRISK [7] features, etc. Detected features are tracked on consecutive frames to find the corresponding points on the frames using techniques such as optical flow [8]. This is followed by a motion-estimation step that typically uses epipolar geometry constraints to incorporate feature-to-feature matching (2D to 2D). From there, motion parameters are calculated, and projection from 3D to 2D is performed to minimize 3D tracked landmarks against the current image frame, and 3D to 3D is incorporated to increase the accuracy of the estimated pose. To perform camera pose optimization, research often uses algorithms such as Bundle adjustment, Kalman Filter, and EKF Graph optimization [1]. For DL-based methods, the steps of feature detection, feature matching, and pose estimation are performed using DL [1]. Currently, most VO estimation methods are evaluated on the KITTI dataset [9; 10; 11], TUM RGB-D SLAM dataset [12], NYUDepth dataset [13], and ICL-NUIM dataset [14]. However, DL methods always need a very large amount of data to train a VO estimation model. With this approach, the more data that are trained, and trained in more contexts, the more accurate the VO estimation results will be. In this paper, we propose a standard dataset called the TQU-SLAM benchmark dataset to evaluate VO estimation methods. Our dataset was collected using an Intel RealSense D435 in an environment comprising the second floor of three interconnected buildings: Building A, Building B, and Building C. This dataset includes 160,631 RGB-D frame pairs. The data were collected over a particular length (FO-D is 230.63 m, OP-D is 228.13 m) and were collected four times and eight times. To see the challenges in the TQU-SLAM benchmark dataset, we tested the proposed dataset using PySLAM [15], with traditional features extracted at the feature-extraction step by feature descriptors, such as ORB [16], ORB2 [17], SIFT [4], SURF [5], BRISK [7], AKAZE [18], and KAZE [18], or features extracted from a DL model such as VGG [19], DPVO [20], and TartanVO [21]. In this paper, we perform VO construction on the entire TQU-SLAM benchmark dataset. Our paper has the following main contributions:  
* We introduce the TQU-SLAM benchmark dataset for evaluating VO models, algorithms, and methods.