Keshav Sangam’s Status Report for 4/23

This week, we focused on integration. As of right now, SLAM is being integrated with path planning, but there are some critical problems we are facing. While the map that our SLAM algorithm builds is accurate, the localization accuracy is extremely poor. This makes it impossible to actually path plan. The poor localization accuracy stems from the fact that when we simulate our lidar/odometry data to test our SLAM algorithm, there is too little noise in the data to show problems with bad performance; thus, it is difficult to tune the algorithm without directly running it on the robot. Manually introducing noise in the simulated data did not help improve the tuning parameters. To solve this problem, we are going to record a ROS bag (a collection of all messages being published across the ROS topics) so that we don’t have to generate simulated data, instead taking sensor readings directly from the robot. By recording this data into a bag, we can replay the data and tune the SLAM algorithm from this. I believe we are still on track since we have a lot of time to optimize these parameters. We also worked on the final presentation slides, and started writing the final report.