Vision-based state estimation of autonomous boats
Ladda ner
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Petersson, Anna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
For any autonomous vehicle, such as a self-driving boat, it is essential to estimate its localisation
accurately. One approach to this problem is to use visual odometry, which is a
purely vision-based state estimation. Today, autonomous boats mainly use global navigation
satellite systems (GNSSs) or inertial measurements units (IMUs) and are commonly
only partly self-driving. In contrast, a camera-based system would be more cost-effective
and function in areas where there are no signals from the GNSS. However, a vision-based
state estimation tends to be not as accurate. This project implemented the algorithm
called direct sparse odometry to investigate how such a monocular localisation system
could replace an IMU and a GNSS. At the same time, the work addressed how this method
and similar kinds of algorithms could be automatically evaluated at a future web-based
platform.
We could show that the algorithm did not perform so well on the chosen sequences.
However, there are indications that a direct method could attain better performance than
a feature-based visual odometry method. The project’s results demonstrate how the architecture
of an algorithm running on the platform can be designed and showed directions
for research of more accurate performance. For example, to use several monocular cameras
or use a full simultaneous localisation and mapping (SLAM) system instead would
probably result in a more precise vision-based state-estimation of a boat. The resulting
algorithm will hopefully work as a reference algorithm for future localisation algorithms
on the mentioned platform. Moreover, the conclusions drawn from what requirements are
put on such algorithms can facilitate the platform’s design and implementation.
Beskrivning
Ämne/nyckelord
direct sparse odometry , visual odometry , visual ego-motion estimation , continuous integration , autonomous surface vehicles , autonomous boats , SLAM