Summary:

Mapping the environment with the purpose of building a 3D model that represents it, is traditionally achieved by trained personnel, using measuring equipment such as cameras or terrestrial laser scanners. This process is often expensive and time-consuming. The use of a robotic platform for such a purpose can simplify the process and enables the use of 3D models for consumer applications or in environments inaccessible to human operators. However, fully autonomous 3D reconstruction is a complex task and it is the focus of several open research topics.
In this thesis, we try to address some of the open problems in active 3D environment reconstruction. For solving such a task, a robot should autonomously determine the best positions to record measurements and integrate these measurements in a model while exploring the environment. In this thesis, we first address the task of integrating the measurements from a sensor in real-time into a dense 3D model. Second, we focus on \emph{where} the sensor should be placed to explore an unknown environment by recording the necessary measurements as efficiently as possible. Third, we relax the assumption of a static environment, which is typically made in active 3D reconstruction. Specifically, we target long-term changes in the environment and we address the issue of how to identify them online with an exploring robot, to integrate them in an existing 3D model. Finally, we address the problem of identifying and dealing with dynamic elements in the environment, while recording the measurements.

In the first part of this thesis, we assume the environment to be static and we solve the first two problems. We propose an approach to 3D reconstruction in real-time using a consumer RGB-D sensor. A particular focus of our approach is its efficiency in terms of both execution time and memory consumption. Moreover, our method is particularly robust to situations where the structural cues are insufficient. Additionally, we propose an approach to compute iteratively the next best viewpoint for the sensor to maximize the information obtained from the measurements. Our algorithm is taylored for micro aerial vehicles (MAV) and takes into account the specific limitations that this kind of robots have.

In the second part of this work, we focus on non-static environments and we address the last two problems. We deal with long-term changes by proposing an approach that is able to identify the regions that changed on a 3D model, from a short sequence of images. Our method is fast enough to be suitable to run online on a mapping robot, which can direct its effort on the parts of the environment that have changed. Finally, we address the problem of mapping fully dynamic environments, by proposing an online 3D reconstruction approach that is able to identify and filter out dynamic elements in the measurements.

In sum, this thesis makes several contributions in the context of robotic map building and dealing with change. Compared to the current state of the art, the approaches presented in this thesis allow for a more robust real-time tracking of RGB-D sensors including the ability to deal with dynamic scenes. Moreover, this work provides a new, more efficient view point selection technique for MAV exploration, and an efficient online change detection approach operating on 3D models from images that is substantially faster than comparable existing methods. Thus, we advanced the state of the art in the field with respect to robustness as well as efficiency.