Model-based Boat Recognition for Urban River Navigation using Waterborne LiDAR and Scan Matching Kazuki Ohira(a*), Tetsu Yamaguchi(a), Nobuaki Kubo(b), Etsuro Shimizu(b), Masafumi Nakagawa(a)
a) Shibaura Institute of Technology, Japan
*ah20091[at]shibaura-it.ac.jp
b) Tokyo University of Marine Science and Technology, Japan
Abstract
In recent years, the development of autonomous boats utilizing communication and sensing technologies has been actively pursued worldwide. However, collisions remain frequent among small boats such as fishing boats. Compared to large ships, such as tankers and container ships, most small boats lack advanced navigational aids and instead rely on visual navigation by their crews. The prolonged hours required for manual operation, such as maneuvering and monitoring the surroundings, place a significant burden on small boat operators. In addition, the Tokyo Metropolitan Government has begun using rivers as commuter routes to alleviate traffic congestion caused by population concentration. However, river transportation presents technical challenges, including narrow channels and numerous obstacles. Therefore, obstacle avoidance functions are required for autonomous boats. Existing methods for boat collision avoidance include position sharing using GNSS and image-based object detection using deep learning techinques such as Faster-RCNN. However, GNSS-based position sharing is unavailable in non-GNSS positioning environments such as under bridges. In addition, deep learning-based image processing requires a large amounts of pre-collected training data featuring boats from various orientations to achive reliable detection. Therefore, we propose a model-based boat detection method from a moving boat with LiDAR and scan matching to recognize surrounding moving boats and collisions automatically. Moreover, we evaluated our methodology through experiments using waterborne LiDAR mounted on a boat in urban rivers.
