Keshav Sangam’s Status Report for 3/27/2022

This week I worked on SLAM for the robot. On ROS Melodic, I installed the Hector SLAM package. There are a few problems with testing its efficacy; notably, the map building relies on the assumption that the LIDAR is held at a constant height while maneuvering the world. The next step is to build a mount on the Roomba for all the components so that we can actually test Hector SLAM. On top of this, I have looked into the IMU’s for the potential option of sensor fusion for localization. By using the iRobot’s wheel encoders and a 6-DoF IMU as the input into a fusion algorithm such as Extended Kalman Filtering (EKF) or Unscented Kalman Filtering (UKF), we could potentially massively increase the localization accuracy of the robot. However, things like bumps or valleys on the driving surface may cause localization errors to propogate through the SLAM algorithm due to the constant height assumption mentioned before. We will have to conduct tests on the current localization accuracy when we get the mount working in order to decide if the (E/U)KF+IMU is worth it.