Estimated trajectory data from our work

This dataset was used to support our work and provided to the review for reference. The TUM RGB-D dataset comprises image sequences from dynamic indoor settings, containing various line densities and scene dynamics conducive for SLAM analysis. We selected four walking and two sitting sequences to demonstrate high and low dynamics, respectively. The walking sequences (w/half, w/rpy, w/static, w/xyz) capture two individuals in motion, including chair movement, while the sitting sequences (s/half, s/xyz) document two individuals in conversation with minimal movements. The appended terms `half', `rpy', `static', and `xyz' specify different camera movements. The KITTI dataset consists of images in natural and urban outdoor settings. The KITTI dataset is a collection of images captured in varied natural and urban outdoor environments. Our research specifically zeroes in on sequences 00 to 10, all of which supply us with verified ground truth data. The sequences we've chosen for analysis are characterized by their unique mix of traffic density and the prevalence of man-made structures. By conducting a comprehensive examination of these sequences, we are able to gauge the effectiveness and robustness of our DPL-SLAM system across an array of different driving environments.

本数据集旨在支撑我们的研究工作，并作为参考资料提供给评审。TUM RGB-D 数据集包含动态室内环境中的图像序列，涵盖了多样化的线条密度和场景动态，有利于 SLAM 分析。我们选取了四段行走和两段静坐序列以展示高动态和低动态的对比。行走序列（w/half, w/rpy, w/static, w/xyz）捕捉了两位运动中的个体，包括椅子的移动，而静坐序列（s/half, s/xyz）则记录了两位个体在交谈中的最小动作。附加的术语 `half', `rpy', `static', 和 `xyz' 分别指代不同的摄像机运动。KITTI 数据集包含自然和城市户外环境中的图像。KITTI 数据集是捕捉于各式自然与城市户外环境的图像集合。我们的研究特别聚焦于序列 00 至 10，所有这些序列均提供了经过验证的地面真实数据。我们所选择的用于分析的序列以其独特的交通密度和人工结构的普遍性为特征。通过对这些序列进行全面的审查，我们得以评估我们的 DPL-SLAM 系统在不同驾驶环境下的有效性和鲁棒性。