Near-Shore Mapping for Detection and Tracking of Vessels

TL;DR

This paper presents a novel approach for near-shore vessel detection and tracking using LiDAR and neural network-based visual segmentation, improving accuracy in close-to-dock scenarios for autonomous surface vessels.

Contribution

It introduces a combined LiDAR and neural network method for precise near-shore vessel detection, addressing land masking limitations and filtering moving objects.

Findings

Enhanced vessel tracking accuracy near docks.

Effective filtering of static and moving objects.

Validated on real-world kayak and cruiser data.

Abstract

For an autonomous surface vessel (ASV) to dock, it must track other vessels close to the docking area. Kayaks present a particular challenge due to their proximity to the dock and relatively small size. Maritime target tracking has typically employed land masking to filter out land and the dock. However, imprecise land masking makes it difficult to track close-to-dock objects. Our approach uses Light Detection And Ranging (LiDAR) data and maps the docking area offline. The precise 3D measurements allow for precise map creation. However, the mapping could result in static, yet potentially moving, objects being mapped. We detect and filter out potentially moving objects from the LiDAR data by utilizing image data. The visual vessel detection and segmentation method is a neural network that is trained on our labeled data. Close-to-shore tracking improves with an accurate map and is demonstrated on a recently gathered real-world dataset. The dataset contains multiple sequences of a kayak and a day cruiser moving close to the dock, in a collision path with an autonomous ferry prototype.