THESIS
2016
xi, 62 pages : illustrations ; 30 cm
Abstract
There have been increasing demands for developing micro aerial vehicles with vision-based autonomy
in complex environments. In particular, the visual-inertial system (VINS), which consists
of only an inertial measurement unit (IMU) and camera(s), forms a great light-weight sensor suite
due to its low weight and small footprint. VINS aims at simultaneous localization and mapping
(SLAM), which constructs a map of an unknown environment while keeping track of a vehicles
location. The perception ability equipped with the robots by SLAM technique plays an essential
role during autonomous flight.
In this work, we study two parts of the SLAM problem: localization and mapping.
For localization, we develop efficient, high-accuracy VINS using probabilistic graph model.
Towards pl...[
Read more ]
There have been increasing demands for developing micro aerial vehicles with vision-based autonomy
in complex environments. In particular, the visual-inertial system (VINS), which consists
of only an inertial measurement unit (IMU) and camera(s), forms a great light-weight sensor suite
due to its low weight and small footprint. VINS aims at simultaneous localization and mapping
(SLAM), which constructs a map of an unknown environment while keeping track of a vehicles
location. The perception ability equipped with the robots by SLAM technique plays an essential
role during autonomous flight.
In this work, we study two parts of the SLAM problem: localization and mapping.
For localization, we develop efficient, high-accuracy VINS using probabilistic graph model.
Towards plug-and-play and highly customizable VINS, we extend our system to address two challenges:
the initialization problem and the calibration problem. We propose a methodology that is
able to initialize velocity, gravity, visual scale, and camera-IMU extrinsic calibration on-the-fly.
Our approach operates in natural environments and does not use any artificial markers. It also does not require any prior knowledge about the mechanical configuration of the system. The proposed
approach also allows generalizing the monocular VINS to multi-camera VINS, which significantly
boosts the systems accuracy and robustness. We made comprehensive experiments in large-scale
indoor and outdoor environments to demonstrate the performance of our system.
Mapping can be treated as a dual problem of localization, as localization makes mapping feasible
and mapping is able to reduce the drift of localization. Building on top of the localization
system, we develop a scalable monocular mapping system to construct and update the surrounding
environments. By utilizing devices GPU the system achieves dense 3D reconstruction at frame
rates. Experiments in both indoor and outdoor environments prove the reconstruction quality is
comparable to systems that need depth sensor or stereo cameras.
Post a Comment