A lidar-only SLAM algorithm for marine vessels and autonomous surface vehicles
Paper in proceeding, 2022

Research into autonomous surface vehicles is noticeably limited in regards to the functionality of the vehicles themselves. Specifically, testing and evaluation typically occurs at speeds considerably lower than what is allowed in an operational setting. For a vessel to be able to take advantage of higher speeds, there must be a robust and tested method for determining localisation and navigation. With an emphasis of development for small vessels with higher impulse capabilities, working in confined and restricted environments, the decision was made to develop a method of navigation that relied solely upon lightweight sensors. For this, a single light ranging sensor was utilised to develop both simultaneous localisation and mapping for the vessel, using the normal distribution transform and iterative closest point methods. Evaluation of the algorithm accuracy as the vessel moved above speeds greater than two metres per second was conducted, and it was feasibly evaluated that there was no observable drift of mapping in horizontal planes, however, there was a accumulated drift in the vertical plane and a transient response in localisation deviation as the vessel changed impulse through the two metre per second window.

Marine systems,

Mapping,

Lidar-based SLAM,

Localisation,

Author

Artur Engström

Student at Chalmers

Domenic Geiseler

Student at Chalmers

Krister Blanch

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems

Ola Benderius

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems

Iván García Daza

University of Alcalá

IFAC-PapersOnLine

24058963 (eISSN)

Vol. 55 31 229-234

14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles CAMS 2022
Kongens Lyngby, Denmark,

Subject Categories

Robotics

Control Engineering

Signal Processing

DOI

10.1016/j.ifacol.2022.10.436

More information

Latest update

1/12/2023