Robots are increasingly deployed in various locations to automate tasks, including in barns. However, in barns cows can obstruct the sensors such as LiDAR or camera, leading to a lack of environmental information. As a result, the robot’s localization system only relies on odometry at those moments, introducing additional uncertainty to the robot’s pose. When the visibility of the environment is restored, the robot may mistakenly believe it is in a location that does not correspond to its actual position.
The first contribution of this master thesis is a novel method for improving the self-localization in barns by implementing a line detection algorithm which is called Line Adaptive Monte Carlo Localization (LAMCL). The novelty is that only line segments are used to detect a localization error instead of corners between different line segments. It also retains the robot’s current pose to filter out fault-detected localization errors. In addition, the new approach is applied in a dynamic environment. The proposed method combines the Split-and-Merge line detection algorithm with AMCL. The detected line segments are compared with the walls in the environment to identify localization errors. When an error is detected, the robot’s pose is adjusted by placing a section of the particles at the location of the error. In this way, the robot can find its true location again.
The second contribution is a new dataset. This new dataset, called DataCow, consists of four recorded routes in a barn with GT on a handful of spots to evaluate the self-localization. DataCow includes the pseudo-2D LiDAR scans and the odometry of a robot driving through a barn. This dataset is used to evaluate the new self-localization method LAMCL. Through the experiments, it has been discovered that this new method improves the system’s recovery ability, but the accuracy and precision are compromised.