Overview

A Measurement Pipeline.

The Arduino Mega gathers sensor data in the field, checks whether a reading is usable for mapping, writes both raw and filtered logs to SD, and hands the accepted points to Python scripts that turn those points into map products.

Workflow

From field reading to map output

  • 1. Capture: GPS, sonar, temperature, IMU, UTC time.
  • 2. Validate: Fix quality, depth stability, motion, spacing.
  • 3. Log: Full record plus accepted mapping dataset.
  • 4. Reconstruct: IDW surfaces, contours, KML, KMZ overlays.
Hardware

The Platform

The system runs on an Arduino Mega, integrating a GPS module, waterproof ultrasonic sensor, DS18B20 water temperature sensor, BNO085 IMU for pitch/roll orientation, an LCD display, and an SD card module for offline field logging.

Validation Logic

Measurement thresholds.

The logger does not just collect sensor output. It defines what counts as an acceptable mapping condition. Those choices show up directly in the constants and helper functions in the Arduino code.

Parameter Current Value Role in the survey
sonarCalSoundSpeed 1500.0 m/s Baseline sound speed used when correcting sonar range.
maxMapHdop 1.8 Rejects GPS fixes with poorer horizontal precision.
maxMapFixAgeMs 2000 ms Rejects stale GPS fixes that no longer match the platform position closely.
maxMapSpeedKmph 8.0 km/h Limits mapping when the platform is moving too quickly.
maxMapPitchDeg 10.0° Rejects mapping points when platform tilt exceeds the allowed pitch range.
Geospatial Data

Depth becomes bathymetry when tied to position.

A single depth value only says how far the bottom was below the sensor at one moment. Bathymetry needs depth plus coordinates so the measurement can become part of a spatial pattern.

Context Attached

The map file records each accepted point with time, latitude, longitude, depth, temperature, satellite count, HDOP, speed, fix age, pitch, roll, and IMU accuracy. That structure keeps the measurement conditions attached to the point.

point,date_utc,time_utc,lat,lng,depth_cm,temp_c,satellites...

Raw vs Mapping Logs

The raw data log preserves the complete field record, including conditions that may not be suitable for mapping. The map log contains only accepted points. That separation makes the reconstruction step more predictable and keeps the record transparent.

Algorithm Implementation

Executing IDW Interpolation.

OMEGA uses inverse distance weighting to estimate values between measured points. The Python scripts build a grid over the survey area and estimate a depth for each grid cell. Nearby measured points receive more weight than distant ones.

scripts/csv_to_png_depth_interpolated.py 
import numpy as np

def idw_interpolation(x, y, z, xi, yi, power=2.0, epsilon=1e-12):
    """
    Inverse Distance Weighting (IDW) spatial interpolation.
    """
    zi = np.zeros_like(xi, dtype=float)
    
    # Iterate across the target 2D raster grid
    for row in range(xi.shape[0]):
        for col in range(xi.shape[1]):
            dx = x - xi[row, col]
            dy = y - yi[row, col]
            dist = np.sqrt(dx * dx + dy * dy)
            
            # Prevent division by zero
            if np.any(dist < epsilon):
                zi[row, col] = z[np.argmin(dist)]
                continue
                
            # Weight = 1 / (d^p)
            weights = 1.0 / np.power(dist, power)
            zi[row, col] = np.sum(weights * z) / np.sum(weights)
            
    return zi

The Math: The weight of a known sonar point decreases as the distance increases. A power of 2.0 (inverse square) is standard for bathymetry, meaning a ping 2 meters away has 4 times less influence than a ping 1 meter away.

Data Pipelines

Offline Python Processing.

The same accepted points can become several different map products. OMEGA provides a suite of offline Python scripts for generating publishable scientific assets directly from raw CSV exports.

  • csv_to_png_depth_interpolated.py: Shows the continuous surface estimated from the accepted points.
  • csv_to_png_depth_contours.py: Turns the surface into depth lines that emphasize shape and slope. Designed for inclusion in formal academic papers.
  • csv_to_kmz_depth_overlay.py: Places a raster surface over its geographic footprint in Google Earth, packaging the result into a compressed KMZ file.