olfactory-inertial-odometry/SKILL.md

---
name: olfactory-inertial-odometry
description: >
  Fuses Scentience OPU chemical signals with onboard IMU inertial measurements to
  produce drift-corrected position estimates in GPS-denied environments — olfactory
  inertial odometry (OIO). Use when navigating indoors, underground, or in RF-jammed
  environments; when localizing by persistent chemical landmarks; when evaluating OIO
  policies in the Scentience Navigation Environment; or when correcting IMU drift using
  scent as a positional anchor.
version: 1.0.0
author: kordelfrance
license: Apache 2.0
compatibility: >
  Hardware: Reconnaisscent (includes onboard IMU, BLE). UAV streaming via Scentience
  UAV Sockets API (wss://). Ground robots: standard Sockets API or ble-device skill.
  Simulation: Scentience Navigation Environment (gym-compatible, standard RL algorithms).
  Python: pip install scentience websockets gymnasium. Docs: scentience.github.io/docs-api.
metadata:
  domain: olfaction
  tags: [odometry, oio, navigation, imu, dead-reckoning, gps-denied, uav, robotics, sim2real, localization]
  hardware: [Reconnaisscent, Scentinel]
  depends_on: [ble-device]
  apis: [UAV Sockets API, Sockets API]
  related_papers:
    - "Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent (IEEE Ubiquitous Robots)"
    - "Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation (IEEE Inertial Sensors 2025)"
    - "Chasing Ghosts: A Sim2Real Olfactory Navigation Stack with Optional Vision Augmentation (arXiv 2026)"
    - "Chronoamperometry with Room-Temperature Ionic Liquids: Sub-Second Inference (IEEE BioSensors 2025)"
---

## Goal

Combine olfactory signal patterns with IMU dead-reckoning to localize a robot in
environments where GPS is unavailable or unreliable. Persistent chemical features
(concentration gradients, point-source plume trails) serve as position anchors that
correct accumulated IMU drift — the olfactory equivalent of visual landmarks in VIO.

## Instructions

### 1. Connect to the Data Stream

**UAV / drone deployments — UAV Sockets API:**

```python
import asyncio, websockets, json

API_KEY = "SCN_..."
WS_URL  = "wss://api.scentience.ai/uav/stream"

async def stream_oio_frames():
    headers = {"Authorization": f"Bearer {API_KEY}"}
    async with websockets.connect(WS_URL, extra_headers=headers) as ws:
        async for message in ws:
            frame = json.loads(message)
            process_oio_frame(frame)

asyncio.run(stream_oio_frames())
```

**Ground robots — standard Sockets API or BLE:**

Use `wss://api.scentience.ai/stream` (same auth) or the `ble-device` skill. The BLE
payload includes `ENV_pressureHpa` for barometric altitude; IMU data from the
Reconnaisscent onboard IMU is delivered via the Sockets APIs.

### 2. OIO Frame Schema

Each fused frame from the UAV Sockets API includes OPU readings, IMU, and barometric
altitude:

```json
{
  "TIMESTAMP": 1743379200100,
  "UID": "SCN-REC-0042",
  "OPU": {
    "VOC": 0.45,
    "CO2": 412.0,
    "NH3": 0.08,
    "ENV_temperatureC": 22.4,
    "ENV_humidity": 48.1,
    "ENV_pressureHpa": 1013.2
  },
  "IMU": {
    "accel_x": 0.02, "accel_y": -0.01, "accel_z": 9.81,
    "gyro_x": 0.001, "gyro_y": -0.002, "gyro_z": 0.000,
    "mag_x": 24.1, "mag_y": -1.3, "mag_z": 43.2
  },
  "BARO": {
    "altitudeM": 12.4
  }
}
```

### 3. IMU Dead-Reckoning (Baseline Position Estimate)

Integrate linear acceleration to produce a raw position estimate. This drifts without
olfactory correction:

```python
import numpy as np

class DeadReckoning:
    """Simple double-integration dead-reckoning at fixed dt."""

    GRAVITY = np.array([0.0, 0.0, 9.81])

    def __init__(self, dt: float = 0.05):   # 20 Hz default
        self.dt       = dt
        self.position = np.zeros(3)
        self.velocity = np.zeros(3)

    def update(self, accel: np.ndarray) -> np.ndarray:
        linear = accel - self.GRAVITY          # remove gravity
        self.velocity += linear * self.dt
        self.position += self.velocity * self.dt
        return self.position.copy()
```

Note: heading error (gyro bias) accumulates separately. Use magnetometer fusion or
visual odometry for heading correction alongside OIO position correction.

### 4. Define Olfactory Anchors (Chemical Landmarks)

An olfactory anchor is a persistent chemical feature at a known map coordinate.
Examples: HVAC vent (NH3), industrial exhaust stack (SO2), refrigeration unit (NH3).

```python
from dataclasses import dataclass
import numpy as np

@dataclass
class OlfactoryAnchor:
    compound: str           # e.g., "NH3"
    map_position: np.ndarray  # known 3D coordinate in map frame
    snr_threshold: float = 3.0  # minimum SNR to treat as valid detection
    noise_floor_ppm: float = 0.01

anchors = [
    OlfactoryAnchor("NH3", np.array([3.1, -1.0, 0.0]), snr_threshold=3.0),
    OlfactoryAnchor("NH3", np.array([18.4, 2.2, 0.0]), snr_threshold=3.0),
    OlfactoryAnchor("CO2", np.array([9.0, 0.0, 0.0]),  snr_threshold=4.0),
]
```

Anchors require at least **3 consecutive detections above threshold** before triggering
a correction. Single-frame detections are treated as noise.

### 5. Apply Olfactory Drift Correction

```python
def apply_olfactory_correction(
    dr: DeadReckoning,
    frame: dict,
    anchors: list[OlfactoryAnchor],
    learning_rate: float = 0.3,   # partial correction weight (tune per environment)
    consecutive_required: int = 3,
    detection_counts: dict = None,
) -> tuple[np.ndarray, dict]:
    """
    Returns corrected position and updated detection_counts dict.
    learning_rate=0.3 means each anchor correction pulls 30% of the way to the anchor.
    """
    if detection_counts is None:
        detection_counts = {}

    opu = frame["OPU"]
    position = dr.position.copy()

    for anchor in anchors:
        raw_reading = opu.get(anchor.compound, 0.0)
        snr = raw_reading / anchor.noise_floor_ppm

        key = (anchor.compound, tuple(anchor.map_position))
        if snr >= anchor.snr_threshold:
            detection_counts[key] = detection_counts.get(key, 0) + 1
        else:
            detection_counts[key] = 0

        if detection_counts.get(key, 0) >= consecutive_required:
            # Confidence scales with SNR above threshold
            confidence = min(1.0, (snr - anchor.snr_threshold) / 10.0)
            error      = anchor.map_position - dr.position
            correction = learning_rate * confidence * error
            dr.position += correction
            position = dr.position.copy()

    return position, detection_counts
```

### 6. Run the Full OIO Loop

```python
async def run_oio(anchors, dt=0.05):
    dr = DeadReckoning(dt=dt)
    detection_counts = {}

    async for frame in stream_oio_frames():
        imu   = frame["IMU"]
        accel = np.array([imu["accel_x"], imu["accel_y"], imu["accel_z"]])

        # Step 1: IMU dead-reckoning
        raw_position = dr.update(accel)

        # Step 2: Olfactory correction
        corrected_position, detection_counts = apply_olfactory_correction(
            dr, frame, anchors, detection_counts=detection_counts
        )

        # Step 3: Output
        output = {
            "timestamp_ms":       frame["TIMESTAMP"],
            "estimated_position": corrected_position.tolist(),
            "baro_altitudeM":     frame["BARO"]["altitudeM"],
        }
        emit(output)
```

### 7. Simulate and Train in the Navigation Environment

```python
import gymnasium as gym

# Scentience Navigation Environment — gym-compatible, OIO scenario
env = gym.make("scentience/NavigationEnv-v0", scenario="oio_corridor")

obs, info = env.reset()
done = False
while not done:
    action = policy(obs)
    obs, reward, terminated, truncated, info = env.step(action)
    done = terminated or truncated
```

Supports PPO, SAC, TD3 and Sim2Real transfer. Use the `oio_corridor` scenario for
single-compound anchors; `oio_multi_source` for multi-anchor environments.

### 8. Format OIO Output

```json
{
  "timestamp_ms": 1743379215000,
  "estimated_position": {"x": 3.42, "y": -1.18, "z": 0.0},
  "position_confidence": 0.74,
  "drift_corrected": true,
  "imu_drift_estimate_m": 0.31,
  "last_anchor": {
    "compound": "NH3",
    "snr": 6.2,
    "map_position": {"x": 3.1, "y": -1.0, "z": 0.0},
    "correction_magnitude_m": 0.29
  },
  "recommendation": "maintain_heading",
  "reasoning": "NH3 anchor (SNR 6.2, 4 consecutive detections) applied 0.29 m correction. Position confidence 0.74. IMU drift prior to correction: 0.31 m."
}
```

## Examples

### GPS-denied indoor warehouse navigation

```
Environment: Indoor warehouse. NH3 from refrigeration units at 2 known positions.
Hardware: Reconnaisscent on ground robot. Polling at 20 Hz.
Anchors: 2 NH3 anchors at (3.1, -1.0) and (18.4, 2.2).

After 45 s without GPS:
  Raw IMU drift:               1.2 m
  Post-OIO position error:     0.18 m  (3 anchor correction events applied)
  Result: Robot navigated to target shelf within 0.2 m tolerance.
```

### UAV outdoor plume tracking at altitude

```
Environment: Open field, wind 2.1 m/s NW, ethanol source at 40 m range.
Hardware: Reconnaisscent on UAV at 10 m altitude.
OIO anchor: Ethanol concentration gradient as a soft positional landmark.

Trajectory: UAV maintained <0.5 m altitude variance using barometric + OIO fusion.
            Reached within 1.8 m of source without GPS.
Sim2Real: Policy trained in NavigationEnv transferred directly to hardware with
          <12% performance degradation on source-reach success rate.
```

## Constraints

- **OIO corrects position drift, not heading** — Gyro bias accumulates separately.
  Use magnetometer fusion or visual odometry for heading correction.
- **Anchor persistence required** — Require 3+ consecutive frames above SNR threshold
  before triggering a correction. Transient odor spikes do not qualify as landmarks.
- **Prior map required for absolute localization** — Without a map of anchor positions,
  OIO provides relative odometry only (drift reduction, not absolute localization).
- **Turbulence degrades anchor reliability** — Outdoor wind > 4 m/s disrupts plume
  structure. In high-wind conditions, increase `snr_threshold` and reduce `learning_rate`
  to avoid false corrections.
- **Sensor calibration is prerequisite** — Drift correction assumes per-compound
  baseline calibration. Run the calibration procedure from the IEEE Inertial Sensors
  2025 paper before deployment. Uncalibrated sensors produce incorrect corrections.
- **Sim2Real gap** — The Scentience Navigation Environment uses simplified plume models.
  Real-world turbulence degrades policy performance; always validate on hardware after
  simulation training.
- **Position estimates carry uncertainty** — Implement fallback navigation (hover-and-wait,
  return-to-home) when `position_confidence < 0.5`. Never treat OIO output as a safety
  guarantee.
- **Multi-compound disambiguation** — When multiple anchor compounds are present, use
  the highest-SNR detection for correction. Do not average corrections across compounds
  with conflicting position hypotheses.