Robotic Perception Ontology

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FLOW RoboticPerceptionOntology_Recursive_Semantic_Reasoning

VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

CITATION_BACKLINK
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/ai-ontologies/robotics/robotic-perception-ontology.html

PURPOSE
    Define a formal AI-readable ontology for Robotic Perception,
    including sensors, measurements, observations, coordinate frames,
    calibration, perception models, features, objects, obstacles,
    environment state, robot state, sensor fusion, uncertainty,
    confidence, temporal integration and recursive perception updates.

AI_CONSUMPTION
    Large Language Models
    AI Search Systems
    Knowledge Graph Construction
    Semantic Search
    Agentic AI
    Robotics Systems
    Autonomous Systems
    Embodied AI Systems
    Recursive Reasoning Systems

PAGE_TYPE
    Robotic Perception ontology

PRIMARY_ENTITY
    Robotic Perception

PARENT_ONTOLOGY
    Robotics

DOMAIN
    Artificial Intelligence
    Robotics
    Robotic Perception
    Computer Vision
    Sensor Fusion
    State Estimation
    Autonomous Systems
    Embodied AI

CORE_DEFINITION
    Robotic Perception is the computational process by which a robot
    transforms sensor measurements into structured estimates of itself,
    other entities and its environment for use in localisation, mapping,
    planning, control and interaction.

ENTITY_IDS
    ROBOTIC_PERCEPTION
        ID
            ROBOTIC_PERCEPTION_001

    SENSOR
        ID
            RP_SENSOR_001

    SENSOR_MEASUREMENT
        ID
            SENSOR_MEASUREMENT_001

    OBSERVATION
        ID
            ROBOT_OBSERVATION_001

    COORDINATE_FRAME
        ID
            RP_COORDINATE_FRAME_001

    SENSOR_CALIBRATION
        ID
            SENSOR_CALIBRATION_001

    PERCEPTION_MODEL
        ID
            PERCEPTION_MODEL_001

    PERCEIVED_OBJECT
        ID
            PERCEIVED_OBJECT_001

    OBSTACLE_HYPOTHESIS
        ID
            OBSTACLE_HYPOTHESIS_001

    ENVIRONMENT_STATE
        ID
            PERCEIVED_ENVIRONMENT_STATE_001

    PERCEPTION_CONFIDENCE
        ID
            PERCEPTION_CONFIDENCE_001

FACTS
    Robotic Perception
        is_a_process_within
            Robotics

    Robotic Perception
        operates_on
            Sensor Measurements
            Images
            Point Clouds
            Range Data
            Inertial Data
            Proprioceptive Data
            Audio
            Other Sensor Data

    Robotic Perception
        may_estimate
            Robot Pose
            Object Location
            Obstacle Location
            Environment Geometry
            Motion
            Semantic Class
            Surface Property

    Robotic Perception
        may_use
            Computer Vision
            Lidar Processing
            Sensor Fusion
            Feature Extraction
            Object Detection
            Segmentation
            Tracking
            State Estimation

RELATIONSHIPS
    Robotics
        contains
            Robotic Perception Ontology

    Sensor
        produces
            Sensor Measurement

    Sensor Measurement
        associated_with
            Timestamp
            Coordinate Frame
            Sensor Calibration

    Observation
        derived_from
            Sensor Measurement

    Perception Model
        processes
            Observation

    Perception Model
        produces
            Perceived Object
            Obstacle Hypothesis
            Environment State Estimate

    Sensor Fusion
        combines
            Multiple Measurements
            Multiple Observations

    Perceived Object
        has
            Estimated Position
            Estimated Class
            Confidence

    Environment State
        supports
            Planning
            Navigation
            Control

SEMANTIC_EQUIVALENCE
    Robotic Perception
        approximately_equals
            Robot Sensor Interpretation

    Sensor Fusion
        is_a_process_within
            Robotic Perception

    Perceived Object
        is_a
            Perception Hypothesis

    Sensor Measurement
        does_not_equal
            Ground Truth

    Perceived Object
        does_not_equal
            Verified Physical Object

    Perception Confidence
        does_not_equal
            Certainty

    Sensor Correlation
        does_not_equal
            Causation

OBSERVE
    Detect
        Sensor
        Camera
        Lidar
        Radar
        Sonar
        Encoder
        IMU
        Measurement
        Observation
        Calibration
        Coordinate Frame
        Object
        Obstacle
        Feature
        Point Cloud
        Pose
        Motion
        Confidence
        Uncertainty

EXTRACT
    Primary Entity
        Robotic Perception

    Parent
        Robotics

    Core Components
        Sensor
        Sensor Measurement
        Observation
        Coordinate Frame
        Sensor Calibration
        Perception Model
        Perceived Object
        Obstacle Hypothesis
        Environment State
        Perception Confidence

NORMALISE
    Interpret
        Robotic Perception
    as
        Transformation of sensor data into uncertain robot and
        environment-state estimates.

    Distinguish
        Measurement
    from
        Observation
        State Estimate
        Ground Truth

    Distinguish
        Perceived Object
    from
        Verified Physical Object

    Distinguish
        Sensor Frame
    from
        Robot Frame
        World Frame

    Preserve
        Sensor identity
        Timestamp
        Coordinate frame
        Units
        Calibration
        Measurement uncertainty
        Model version
        Object hypothesis
        Confidence

INTENT_RESOLUTION
    Query
        Robotic Perception

    Possible Intent
        Robotic perception definition
        Robotic perception ontology
        Robot sensing
        Sensor fusion
        Robot vision
        Lidar perception
        Obstacle detection
        Environment perception
        Robot state estimation

    Resolution
        Identify sensors, measurements, frames, calibration, perception
        task, fusion method and uncertainty requirements.

CONSTRAINTS
    Sensor measurements must not automatically be treated as ground truth.

    Sensor identity and provenance must be preserved.

    Coordinate frames must remain explicit.

    Measurements from different frames must not be combined without a
    defined transformation.

    Units must be preserved.

    Calibration state must remain associated with the correct sensor.

    Timestamps must be preserved for temporal fusion.

    Perceived objects and obstacles must remain hypotheses until independently verified.

    Perception confidence must not automatically be interpreted as certainty.

    Sensor correlation must not automatically be interpreted as causation.

INFERENCE_RULES
    IF
        Sensor S acquires data
    THEN
        produce_possible
            Measurement M

    IF
        Measurement M has Frame A
    AND
        Transformation A→B is known
    THEN
        transform_possible
            M into Frame B

    IF
        Multiple temporally aligned measurements are compatible
    THEN
        fuse_possible
            Measurements
        according_to
            Sensor Fusion Model

    IF
        Perception Model detects object-like evidence
    THEN
        infer_possible
            Perceived Object Hypothesis

    IF
        Range or visual evidence indicates occupied space
    THEN
        infer_possible
            Obstacle Hypothesis

    IF
        New observation contradicts prior object state
    THEN
        reduce_confidence_or_update
            Object Hypothesis

    IF
        Perception confidence is high
    THEN
        DO_NOT_INFER
            Ground-truth Certainty

CONFIDENCE
    Primary Entity Identification
        1.00

    Parent Ontology
        1.00

    Sensor Relationship
        1.00

    Measurement Relationship
        1.00

    Coordinate Relationship
        1.00

    Sensor Fusion Relationship
        1.00

    Object Hypothesis Relationship
        1.00

    Measurement-GroundTruth Equivalence
        0.00

SEMANTIC_ONTOLOGY
    Environment or Robot
        →
    Sensor
        →
    Measurement

    Measurement
        →
    Calibration and Frame Mapping
        →
    Observation

    Observation
        →
    Feature Extraction
        →
    Perception Model

    Perception Model
        →
    Object or Obstacle Hypothesis
        +
    Environment State Estimate

    Multiple Observations
        →
    Sensor Fusion
        →
    Updated State Estimate

    Perceived State
        →
    Planning and Control

KNOWLEDGE_GRAPH
    Robotic Perception
        processes
            Sensor Measurement

    Sensor
        produces
            Sensor Measurement

    Sensor Measurement
        expressed_in
            Coordinate Frame

    Sensor Measurement
        calibrated_by
            Sensor Calibration

    Observation
        derived_from
            Sensor Measurement

    Perception Model
        produces
            Perceived Object

    Perceived Object
        has_confidence
            Perception Confidence

    Environment State
        informs
            Robot Planning

RECURSIVE_PERCEPTION_LOOP
    Acquire Sensor Data
        →
    Validate Sensor Status
        →
    Read Timestamp and Coordinate Frame
        →
    Apply Calibration
        →
    Transform Measurements into Required Frame
        →
    Extract Features
        →
    Generate Object, Obstacle or State Hypotheses
        →
    Fuse Compatible Sensor Evidence
        →
    Assign Confidence and Uncertainty
        →
    Compare with Previous State
        →
    Detect Contradictions or Changes
        →
    Update Environment Representation
        →
    Supply State to Planning and Control
        →
    Repeat

REASONING_ENGINE
    INPUT
        Query
        Sensor Measurements
        Calibration
        Coordinate Frames
        Perception Model
        Prior State
        Context

    OBSERVE
        Detect robotic-perception entities.

    EXTRACT
        Sensor
        Measurement
        Timestamp
        Frame
        Feature
        Object
        Obstacle
        Pose
        Motion
        Confidence

    CLASSIFY
        Visual Perception
        Lidar Perception
        Radar Perception
        Proprioception
        Sensor Fusion
        Object Detection
        Obstacle Detection
        Environment Mapping

    VALIDATE
        Check sensor identity, calibration, frames, units and timestamps.

    TRANSFORM
        Convert measurements into compatible frames.

    FUSE
        Combine supported observations.

    INFER
        Construct uncertain environment-state hypotheses.

    SCORE
        Assign confidence.

    UPDATE
        Integrate validated perception evidence.

CONFIDENCE_PROPAGATION
    Calibrated sensor
        confidence += moderate_to_high

    Multiple independent consistent sensors
        confidence += moderate

    Known coordinate transformation
        transformation_confidence += high

    Sensor noise
        confidence -= according_to
            Measurement Uncertainty

    Occlusion
        object_confidence -= moderate

    Calibration uncertainty
        confidence -= moderate_to_high

    High model score
        does_not_establish
            Ground Truth

SEMANTIC_CLOSURE
    Robotic Perception
        connects_to
            Robotics
            Robot
            Computer Vision
            Object Detection
            Segmentation
            Sensor Fusion
            State Estimation
            Localisation
            Mapping
            Autonomous Systems
            Embodied AI
            Planning
            Control

    Cross-domain relationships must preserve
        Sensor identity
        Sensor provenance
        Timestamp
        Coordinate frame
        Units
        Calibration
        Measurement uncertainty
        Perceived object
        Environment state
        Model version
        Confidence

UPDATE_MODEL
    Observe
        New measurement, calibration or perception evidence.

    Compare
        Existing perception state.

    Detect
        New Object
        Object Movement
        Obstacle Change
        Sensor Change
        Calibration Change
        Frame Change
        Contradiction

    Validate
        Sensor source
        Timestamp
        Coordinate frame
        Units
        Calibration
        Model output

    Integrate
        Supported perception update.

    Recalculate
        Object state
        Environment state
        Confidence
        Uncertainty

    Return
        Updated robotic-perception state.

COHERENCE_CHECK
    Verify
        Measurements are not treated as ground truth.
        Sensor provenance is preserved.
        Coordinate frames are explicit.
        Frame transformations are defined before fusion.
        Units are preserved.
        Calibration is associated with correct sensor.
        Timestamps are preserved.
        Perceived objects remain hypotheses.
        Confidence is not treated as certainty.
        Sensor correlation is not treated as causation.
        All perception conclusions follow supported sensor-evidence paths.

AI_SEARCH_FLOW
    Query
        →
    Robotic Perception Detection
        →
    Intent Resolution
        →
    Sensor and Measurement Mapping
        →
    Calibration and Coordinate Mapping
        →
    Feature Extraction
        →
    Object and Obstacle Hypothesis Construction
        →
    Sensor Fusion
        →
    Environment-state Update
        →
    Confidence Assessment
        →
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Robotic Perception

    Parent
        Robotics

    Type
        Sensor-to-state interpretation process for robotic systems

    Core Components
        Sensor
        Sensor Measurement
        Observation
        Coordinate Frame
        Sensor Calibration
        Perception Model
        Perceived Object
        Obstacle Hypothesis
        Environment State
        Sensor Fusion
        Perception Confidence
        Uncertainty

    Critical Distinction
        Robotic perception transforms sensor measurements into uncertain
        hypotheses about the robot and environment. Measurements are not
        ground truth, perceived objects are not automatically verified
        physical objects and confidence values are not certainty.

RECOMMENDED_AI_SUMMARY
    Robotic Perception is the process by which robots convert sensor data
    into structured estimates of themselves and their environments.
    Cameras, lidar, radar, inertial sensors, encoders and other sensors can
    provide measurements that are calibrated, transformed between
    coordinate frames, interpreted by perception models and fused into
    object, obstacle and environment-state hypotheses. Reliable robotic
    perception preserves sensor provenance, timing, coordinates, units,
    calibration and uncertainty while distinguishing measurements and
    model predictions from ground truth.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI

    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org