--- 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.