OMEGA

ROV bathymetric mapping pipeline diagram

Data pipeline from ROV movement to bathymetric surface generation.

Quick Start

python csv_to_png_depth_points.py MAP00.csv
python csv_to_png_depth_interpolated.py MAP00.csv
python csv_to_png_depth_contours.py MAP00.csv
python csv_to_kml_points_colored.py MAP00.csv
python csv_to_kmz_depth_overlay.py MAP00.csv

Oceanic Measurement & Environmental Geospatial Array

A compact underwater survey system that collects depth, position, and environmental data from a moving ROV and converts those measurements into bathymetric maps and georeferenced outputs. Measurements are filtered in real time using quality and spatial constraints, producing both a complete log and a reduced mapping dataset. A continuous surface is generated from the filtered points through spatial interpolation.

Overview

The system is built around an Arduino-based capture device and a Python-based processing workflow.

It is designed to:

record reliable field data in real time
filter out low-quality measurements
generate usable spatial maps from irregular samples
visualize results as raster maps and georeferenced overlays

Repository Structure

arduino/
  rov_logger_mapping.ino

docs/
  SYSTEM.md
  SCRIPTS.md

scripts/
csv_to_png_depth_points.py 
csv_to_png_depth_interpolated.py 
csv_to_png_depth_contours.py
csv_to_kml_points_colored.py 
csv_to_kmz_depth_overlay.py

example_data/
  MAP00.CSV

System Architecture

flowchart LR
    A[GPS Module] --> G[Arduino Logger]
    B[Ultrasonic Depth Sensor] --> G
    C[DS18B20 Temperature] --> G
    D[BNO085 IMU] --> G
    E[SD Card] <-->|logs| G
    F[LCD] <-->|status| G

    G --> H[dataXX.csv]
    G --> I[mapXX.csv]

    I --> J[Python Scripts]
    J --> K[Depth Map]
    J --> L[Interpolated Surface]
    J --> M[Contour Map]
    J --> N[KML Output]
    J --> O[Earth Overlay]

Architecture Overview

Layer	Function
Capture (Arduino)	GPS (position + UTC), sonar depth, temperature, IMU orientation, SD logging
Processing (Python)	CSV parsing, filtering, interpolation (IDW), contour generation
Output	Depth maps, contour maps, KML files, Google Earth overlays

Hardware

Components

Arduino Mega
GPS module (UART)
Waterproof ultrasonic distance sensor
DS18B20 temperature sensor
BNO085 IMU
I2C 16×2 LCD
SD card module

Wiring Summary

Component	Connection
GPS	RX1 (19), TX1 (18)
Ultrasonic	RX2 (17), TX2 (16)
SD Card	CS pin 53
Temperature Sensor	Pin 6
LCD / IMU	SDA (20), SCL (21)

Data Pipeline

1. Data Capture

The Arduino logger writes two files:

dataXX.csv — complete system log (diagnostics)
mapXX.csv — filtered survey dataset

Mapping points are recorded only when the system detects:

valid GPS fix
acceptable HDOP
sufficient satellite count
recent fix age
valid depth reading
stable orientation (pitch/roll)
acceptable platform speed
minimum spacing from the previous point

2. Processing

Run scripts on the mapping dataset:

python csv_to_png_depth_points.py MAP00.CSV
python csv_to_png_depth_interpolated.py MAP00.CSV
python csv_to_png_depth_contours.py MAP00.CSV
python csv_to_kml_points_colored.py MAP00.CSV
python csv_to_kmz_depth_overlay.py MAP00.CSV

3. Outputs

Scatter map — validation of raw coverage
Interpolated map — continuous surface model
Contour map — readable bathymetry
KML + overlay — georeferenced visualization

Key Features

UTC Time Logging

All timestamps are recorded in UTC to eliminate timezone ambiguity.

Depth Smoothing

A moving average filter reduces sonar noise and rejects transient spikes.

Sound Speed Correction

Depth is adjusted using a temperature-based estimate of sound speed.

Real-Time Data Filtering

Measurements are evaluated during acquisition to ensure mapping data meets defined quality thresholds.

Spatial Interpolation

Inverse Distance Weighting (IDW) converts discrete samples into continuous surfaces.

Data Format

Mapping File (`mapXX.csv`)

point,date_utc,time_utc,lat,lng,depth_cm,temp_c,satellites,hdop,speed_kmph,fix_age_ms,pitch_deg,roll_deg,imu_acc

Outputs at a Glance

dataXX.csv — complete acquisition log
mapXX.csv — filtered survey dataset
*_depth_map.png — raw point coverage
*_interpolated.png — interpolated surface
*_contours.png — contour visualization
*_colored.kml — colorized Google Earth points
*_overlay.kmz — Google Earth ground overlay

Example Workflow

python csv_to_png_depth_points.py MAP00.csv
python csv_to_png_depth_interpolated.py MAP00.csv
python csv_to_png_depth_contours.py MAP00.csv
python csv_to_kml_points_colored.py MAP00.csv
python csv_to_kmz_depth_overlay.py MAP00.csv

Example Output

Running the scripts produces:

*_depth_map.png
*_interpolated.png
*_contours.png
*_colored.kml
*_overlay.kmz

Method

This project implements a self-contained bathymetric survey system using a mobile ROV platform.

As the device moves, it continuously samples depth, position, and environmental data. Each measurement is evaluated in real time against defined quality constraints, including GPS validity, HDOP, satellite count, fix age, platform orientation, and spatial separation.

The system produces two datasets:

a complete log (dataXX.csv) containing all measurements
a filtered mapping dataset (mapXX.csv) containing only survey-quality points

The mapping dataset is generated by enforcing minimum spacing between points and rejecting measurements that do not meet stability or accuracy thresholds.

A continuous surface is constructed from the filtered dataset using spatial interpolation (Inverse Distance Weighting). The resulting map is a derived model, determined by sampling density, platform motion, and filtering criteria rather than direct sensor output.

This architecture separates acquisition, validation, and surface reconstruction into distinct stages, allowing control over data quality during both capture and processing.

Limitations

Ultrasonic sensors have beam spread and limited range underwater
GPS accuracy limits spatial resolution
Sound speed correction uses temperature only
Interpolation produces a modeled surface, not direct observation
Map quality depends on survey spacing, speed, and stability

Future Improvements

pressure-based depth sensor integration
salinity-aware sound speed correction
real-time mapping preview
automated survey path planning
higher-resolution interpolation methods
contour labeling and hillshading
live telemetry and visualization

Documentation

System Documentation

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