Seattle, Washington, United States
During my three months with FIL, I worked independently on developing and testing different features for an AMR in a simulation setup using Gazebo and ROS Melodic framework. I simulated a custom mobile robot in Gazebo by writing URDFs and synthesized data from fake sensors as a close emulation of the real world. I implemented gyrodometry and verified the sensor fusion results using RQT, RViz and other visualization tools. I also implemented features like geo-fencing and interface functionalities. I explored different SLAM techniques (like RTAB, Gmapping and hector) in ROS.
In the second phase, I focused on detection of dynamic obstacles and inclined surfaces in the simulated environment. I analyzed the influence of dynamic objects on gmapping and AMCL, and worked on their detection. I, then, implemented detection of humans in 2D laser data using a random forest classifier. Finally, I got to implement detection of inclined surfaces from 3D point clouds with an accuracy of 0.02 meters in ramp width and of 10 degrees in slope angle estimation, followed by integrating these modules to the mapping and navigation modules.
Working independently, I gained experience in setting my own deadlines to meet the company's expectations, directing research and converting those ideas to plausible implementations. I also had the opportunity to learn new software tools, that I wasn't familiar with but were needed for the work, without letting it affect the deadlines.