TY  - JOUR
T1  - Nautical Chart Understanding for Autonomous Surface Ship Operations
AU - Singh, R. Durga 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 15
SP  - 3957
EP  - 3960
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.3957.3960
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.3957.3960
KW  - Obstacle avoidance
KW  -ENCs
KW  -ASV
KW  -MOOS-IVP
KW  -LIDAR
KW  -India
AB  - When a mariner navigates into an unfamiliar area, he/she uses a nautical chart to familiarize
him/herself with the environment, determine the locations of hazards and decide upon a safe course of travel.
An Autonomous Surface Vehicle (ASV) would gain a significant advantage if, like its human counterpart, it can
learn to read and use the information from a nautical chart. Electronic Nautical Charts (ENCs) contain extensive
information on an area, providing indications of rocks and other obstructions, navigational aids, water depths
and shorelines. The goal of this research is to increase an ASV’s autonomy by using ENCs to guide the helm
when its predetermined path which may be dynamically changing is unsafe due to known hazards to navigation
and context to its sensor measurements that are invariably subject to uncertainty. Identifying objects in a
camera, sonar, LIDAR or other sensor’s data can be a challenging endeavor in an ocean environment due to
the variable sea state, the wind, fog, sea spray, sun glint from the sea surface and bubbles in the water column.
Therefore, providing a prior probability distribution for the likely location of those objects in a sensor’s field
of view has the potential to enhance object detection processing significantly. Contextualizing sensor
measurements dynamic identifies objects from the ENC in a sensor’s field of view and provides that information
to the sensor in real-time. It accomplishes these tasks, feature layers within a standard ENC must be translated
to a spatial database. In this database, features are encoded with a “threat level” based on the feature type and
the estimated depth of the object which is not always encoded within the ENC. Variations in the political tides
as well as the vessel size and speed are also factors when deciding the threat level and the vehicle’s appropriate
course of action.
ER  -