ISP2021-visual_slam

This is the github project for the F1Tenth Independent Study Projects 2021. In this project we want to develop a VISUAL SLAM algorithm that is capable creating a map based on camera data as well as localize the car based on camera data.

Software Requirements

• Linux Ubuntu (tested on versions 18.04)
• ROS Melodic.
• Python 3.69.

Hardware Requiremenrts

• Realsense D435i
• Nvidia Jetson TX2

Dependencies

• Librealsense2
• Kimera
• Segmentation Pipeline

Installation

• Clone the current repository in a new workspace.
• Install Librealsense2 on your system follow the instruction listed here.
• Install Librealsense2 on the Jetson along with the kernel updates of the latest version use the instructions listed here.
• Setup and install Kimera VIO ROS and the Kimera - Semantics
• Install the Segmentation Pipeline in your current workspace.

Running the code for 3D reconstruction of map

• Start ROS Master for communication between nodes
  roscore

• Launch the Intel RealSense camera node with the following parameters
  roslaunch realsense2_camera rs_camera.launch enable_gyro:=true enable_accel:=true enable_infra1:=true enable_infra2:=true unite_imu_method:=linear_interpolation infra_width:=848 infra_height:=480 infra_fps:=15

• Disable the camera IR emitter
  rosrun dynamic_reconfigure dynparam set /camera/stereo_module emitter_enabled 0

• Source your workspace
  source ~/devel/setup.bash

• Launching the Kimera VIO node with the follwing parameters with Loop closure detection (use_lcd) enabled
  roslaunch kimera_vio_ros kimera_vio_ros_realsense_IR.launch run_stereo_dense:=true should_use_sim_time:=false use_lcd:=true

• Launch Kimera Semantics node
  roslaunch kimera_semantics_ros kimera_metric_realsense.launch run_stereo_dense:=true online:=true register_color:=true use_sim_time:=false

• Visualise the Reconstruction on RViz
  rviz -d $(rospack find kimera_semantics_ros)/rviz/kimera_semantics_euroc.rviz