Visual Odometry with Inertial and Depth (VOID) dataset
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Updated
Jul 31, 2024 - Shell
Visual Odometry with Inertial and Depth (VOID) dataset
RGBD-3DGS-SLAM is a monocular SLAM system leveraging 3D Gaussian Splatting (3DGS) for accurate point cloud and visual odometry estimation. By integrating neural networks, it estimates depth and camera intrinsics from RGB images alone, with optional support for additional camera information and depth maps.
Structure from Motion modules for UIPF
Docker repo for details on how to use Kimera-ROS-Librealsense docker image hosted on docker hub
how Install Opencv and Open3d in jetson nano with cuda support
An API to interactively run the 3D reconstruction software PyReconstruct on the Lonestar6 supercomputer.
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