Lidar-based simultaneous localisation and mapping in marine vehicles: Handling the complex motions in a marine setting

Examensarbete för masterexamen
Complex adaptive systems (MPCAS), MSc
Engström, Artur
Geiseler, Domenic
To perform precise and safe manoeuvres in an autonomous vehicle, it is important to be able to extract its position in relation to its surroundings. Furthermore, an accurate depth representation of the environment using a lidar scanner helps to detect distances and possible dangers. Simultaneous localisation and mapping (SLAM) is a well-researched tool to aid autonomous cars in operation and data collection to solve both of these tasks at once. The less explored domain of SLAM on a marine vessel presents more complex kinematic movements and is vulnerable to drift in the vertical plane. This master thesis is part of the Reeds data set project, which aims to provide researchers with sensor data to support projects in marine settings. In this work, it was investigated how to reduce this vertical drift using hardware and software-based approaches and what the minimal conditions for performing SLAM are. The baseline performance was built on the lidar-only NDT and ICP registration algorithms. Data loggings were recorded using a high-performance lidar with additional sensor data from an inertial measurement unit and a global navigation satellite system. The latter sensors were fused in an Unscented Kalman filter to estimate the sensor position and thereby improve mapping. The hardware-based approach used a gimbal stabiliser to constrain movement to rotations on the yaw axis. In the analysis, it was shown that both approaches were able to significantly reduce deviation compared to the lidar-only approach. While the UKF state estimation performed better in situations with very sparse point clouds, the gimbal approach offered computational advantages due to the rotational restrictions. Both approaches can be combined to benefit from each other. The minimal conditions for SLAM were bound to the point-cloud density and velocity, as was shown by correlation. These findings can aid in future data recordings regarding the building of a sensor platform.
SLAM , UKF , Gimbal , Mapping , Localisation , Marine systems , Marine datasets , Lidar
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