Software and Hardware Infrastructure for Visual-Inertial SLAM
Name
Mohamoud-mohamoud-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
83.32 MB
Format
Adobe PDF
Checksum (MD5)
18c7a330692aa48aa26444d056cc9671
Author(s)
Mohamoud, Mubarik M.
Advisor(s)
Carlone, Luca
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
One of the challenges faced by researchers in the field of robot localization and mapping is finding a reliable infrastructure to test their ideas. That infrastructure could be a simulation platform, suitable hardware, or a sensor interface. A useful simulation platform needs to capture the dynamics and the sensor modalities that meet the researchers’ needs. A suitable hardware needs to have the capability to navigate, sense the environments, and use onboard computers to run the software it was designed for. A sensor interface allows adapting and testing algorithms on novel sensors. In this research, we develop an essential hardware and software infrastructure for aiding the development and testing of visual-inertial Simultaneous Localization and Mapping (SLAM) systems. SLAM is a fundamental problem in robot navigation and enables constructing or updating a representation (map) of an environment utilizing sensors on board a robot while concurrently using that representation to localize the robot itself. In visual-inertial SLAM the onboard sensors are cameras (monocular or stereo) and an inertial measurement unit (IMU). The contribution of this thesis is threefold. First, we develop a hardware platform consisting of a real drone capable of running state-of-art metric-semantic SLAM; this infrastructure allows us to test advanced SLAM algorithms using real sensors and real robot dynamics. Second, we develop a multi-robot simulation platform that includes dynamically accurate, photo-realistic drones; this platform allows extending our tests to multi-robot SLAM systems. Finally, we develop a new sensor interface; in particular, we integrate and test an omnidirectional stereo frontend in Kimera, an open-source visual-inertial SLAM pipeline. The thesis presents the design, implementation, and testing of each contribution.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
