This work is an optimized version of FLOAM which uses an IMU to aid odometry estimation

Modifier: Daniel Adolfsson,

• IMU is used to deskew point cloud in laserProcessingNode.cpp

  • Assumpitions: high frequency, no linear motion (not valid - please fix)

• IMU is used to predict movement of sensor

  • This is quickly implemented directly in laserProcessingNode.cpp - a side effect is that estimated orientation will appear fixed.

• Changed from Huber to Cauchy loss funciton. Cauchy should be 5-30% better with a good initial guess

  • Assumes a good initial guess

• Compesate for linear velocity - to gain up to 0.1m less noise for motion at 1m/s
• Improve map represetation in scan matcher

IMU. It is assumed that the IMU to lidar extrinsic parameters are eulerRPY=[0, 0, 180] deg

Ubuntu 64-bit 18.04.

ROS Melodic. ROS Installation

Follow Ceres Installation.

Follow PCL Installation.

For visualization purpose, this package uses hector trajectory sever, you may install the package by

sudo apt-get install ros-melodic-hector-trajectory-server

Alternatively, you may remove the hector trajectory server node if trajectory visualization is not needed

    cd ~/catkin_ws/src
    git clone https://github.com/dan11003/floam
    cd ..
    catkin_make
    source ~/catkin_ws/devel/setup.bash

    roslaunch floam structor.launch

if you would like to create the map at the same time, you can run (more cpu cost)

    roslaunch floam floam_mapping.launch

If the mapping process is slow, you may wish to change the rosbag speed by replacing "--clock -r 0.5" with "--clock -r 0.2" in your launch file, or you can change the map publish frequency manually (default is 10 Hz)