Figure 1 shows the working and integration of the NVIDIA ISAAC ROS Visual SLAM and Nvblox pipeline. Here, the VSLAM estimates poses and Nvblox reconstructs the map of the surrounding environment for autonomous navigation of the robot.

NVIDIA Isaac ROS VSLAM is a ROS2 package that performs simultaneous stereo visual localization and mapping. It calculates stereo visual inertial odometry using a time-synchronized pair of stereo images and uses Isaac Elbrus GPU-accelerated library. Visual inertial odometry, optimization, and mapping are the three components of Isaac ROS VSLAM. Visual odometry (VO) is a technique for estimating the position of the camera relative to its starting point. The VO pose shown in Figure 1 is estimated by VO. The Visual Simultaneous Localization and Mapping (VSLAM) method is built on top of the VO pose and aims to improve the quality of VO poses by loop closure detection and estimating SLAM poses using previously seen parts of the environment. In addition to visual data, Elbrus can use measurements from an Inertial Measurement Unit (IMU). When VO is unable to estimate a pose, such as when there is poor lighting or a long featureless surface in front of the camera, it switches to IMU. Figure 1 shows the estimated SLAM poses by V-SLAM.

NVIDIA Isaac ROS Nvblox is a ROS2 package used for real-time 3D reconstruction of the environment around AMR from camera images. The reconstruction is intended to be used by path planners to generate a safe navigation path. Nvblox makes use of NVIDIA CUDA to accelerate this process and enable real-time operation. This repository contains ROS2 integration for the Nvblox core library. The Nvblox algorithm is comprised of three key components: the truncated signed distance function (TSDF), the mesh, and the Euclidean signed distance function (ESDF). Nvblox uses an RGB image, a depth image, and a SLAM pose as input and outputs meshes for visualization, and a distance slice map for path planning. Nvblox generates a 2D distance map slice and a 3D mesh from a stream of RGB and depth images and the corresponding pose of the depth image. The RGB image stream, which is used to color the 3D reconstruction of the surrounding environment for visualization, is optional. The 2D distance map slice shows the distance between each point and the nearest reconstructed obstacle and a 3D mesh for RVIZ visualization. the 3D reconstruction of the surrounding environment for visualization, is optional. The 2D distance map slice displays the distance between each point and the nearest reconstructed obstacle, as well as a 3D mesh for RVIZ visualization.