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  • DEIO // EVENT CAMERAS
  • MICROSECOND MOTION
  • 1000× DYNAMIC RANGE

KAIROS

THE DECISIVE MOMENT

Standard cameras miss motion in low light and fail in high dynamic range. Event cameras record pixel-level brightness changes — 10× lower latency, 1000× better dynamic range, no motion blur. KAIROS delivers production-ready event-inertial odometry with stereo event camera integration and a clear path from simulation to discrete neuromorphic hardware.

MODULE STATUS: ACTIVE

MODULE STATUS

10μs

DIVISION
ANIMA
WAVE
W3
DOMAIN
PERCEPTION
WAVE 3 // ANIMA SUITE
EVENT-INERTIAL ODOMETRY
KAIROS // W3 // 040/079
01THE CHALLENGE

FRAME-BASED CAMERAS ARE BLIND TO THE DECISIVE MOMENT

Standard cameras are built for daylight. They miss motion in low light, fail in high dynamic range (sun glare + shadows), and waste compute on static regions. Event cameras solve this by recording pixel-level brightness changes — not frames — with microsecond precision.

But event cameras are rare in production robotics. Reference datasets don't cover manipulation tasks. Processing pipelines are closed-source. Most integrators lack the IMU fusion expertise to ship event-inertial odometry at scale. KAIROS bridges this gap.

02THE SOLUTION

WHAT KAIROS DELIVERS

KAIROS is a production-grade event-inertial odometry service. Process events in real-time from stereo event cameras, fuse with IMU, fall back gracefully.

CAPABILITIES

  • DEIO-style tracking, optimization, and pose estimation in real-time
  • Tight coupling between event integration and inertial measurements
  • Event representations: count grids, time surfaces, voxel grids
  • Replay sessions for deterministic testing and demo automation
  • Stereo event camera support (Inivation, iniVation DAVIS) + frame-to-event simulation fallback
  • 11 REST endpoints: events, IMU, trajectory, state, calibration
  • MLX Apple Silicon (M1–M5, ~10ms event processing on M5) + CUDA support
03ENGINEERING

WHY THIS IS HARD

Event cameras output asynchronous spike trains, not frames. Standard CV doesn't work:

  1. 01Feature tracking in continuous time, not discrete frames — SIFT/SURF/ORB are useless
  2. 02DEIO decouples event-tracking from IMU preintegration for independent optimization
  3. 03Sensor fusion: buffer events until IMU measurements arrive, maintain consistent state
  4. 04Calibration bundle (intrinsics, extrinsics, IMU bias) must be stable for production
  5. 05Factor-graph optimization: deterministic, no randomness, suitable for safety-critical systems

KAIROS implements DEIO with decoupled event-inertial tracking, persisted calibration bundles, and a deterministic core suitable for production deployment.

04BUILD STATUS

WHAT'S BUILT TODAY

9/11 COMPONENTS COMPLETE
WHAT'S BUILT TODAY
COMPONENTSTATUSNOTES
Core modelsCOMPLETEDEIO tracker + optimizer + pipeline — production-ready
DEIO CoreCOMPLETETracker, optimizer, pipeline, state management
IMU FusionCOMPLETEBuffering, preintegration, factor graphs
Event ProcessingCOMPLETETime surfaces, voxel grids, event counts
CalibrationCOMPLETEPersisted bundles with IMU statistics
Simulation FallbackCOMPLETEFrame-to-event simulation when hardware unavailable
Replay SystemCOMPLETEDeterministic offline sessions
REST APICOMPLETE11 endpoints: health, events, IMU, trajectory, state
Quality GatesCOMPLETEruff, pytest, mypy — passing on macOS arm64
API layerIN PROGRESSPublic API — pending dataset infrastructure
Event HardwareORDEREDDVS/neuromorphic camera integration pending arrival
05APPLICATIONS

WHERE KAIROS DEPLOYS

  • APP_01

    LOW-LIGHT ROBOTICS

    Underground inspection, night operations, dim warehouses — see where cameras can't.

  • APP_02

    HIGH-SPEED PICK & PLACE

    Motion blur kills traditional cameras. Event cameras capture microsecond changes.

  • APP_03

    MANIPULATION RESEARCH

    Low-latency vision in cluttered grasping tasks with sub-millisecond event streams.

  • APP_04

    MOBILE MANIPULATION

    Event-IMU odometry for fast mapping on mobile manipulation fleets.

  • APP_05

    SPACE & AEROSPACE

    Radiation-hardened event sensors for extreme environments.

  • APP_06

    INDUSTRIAL HDR

    Sun glare + shadows simultaneously — 1000× dynamic range handles it all.

06TECHNOLOGY

UNDER THE HOOD

FOUNDATION: DEIO

  • Decoupled Event-Inertial Odometry
  • Event-inertial tracking with async sensor fusion
  • Factor-graph optimization for pose and bias estimation
  • Deterministic core — no randomness, suitable for production

KAIROS IMPLEMENTATION

  • Python runtime with MLX (Apple Silicon) and CUDA support
  • Event representations: count grids, time surfaces, voxel grids
  • IMU preintegration with Gaussian noise modeling
  • Persisted calibration bundles with conservative statistics
  • 11 REST endpoints for real-time and offline workflows

INTEGRATION POINTS

  • Feeds egocentric odometry to SYNTHESIS (collaborative SLAM)
  • Provides low-light trajectory estimates for manipulation modules
  • Outputs calibration artifacts for downstream sensor fusion (HARMONIA)
07PAPERS

RESEARCH BASIS

  1. [01]DEIO: Decoupled Event-Inertial Odometry — event camera processing for production robotics