sensor-fusion | babysitter | ClaudePluginHub

Skill

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sensor-fusion

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411

Description

Multi-sensor fusion algorithms for perception in autonomous driving

AI Summary

Develops multi-sensor fusion algorithms for object detection and tracking in autonomous vehicle perception systems.

Install

1

Run in your terminal

$

npx claudepluginhub a5c-ai/babysitter

Tool Access

This skill is limited to using the following tools:

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Skill Content

Sensor Fusion Skill

Purpose

Enable multi-sensor fusion algorithm development for autonomous driving perception including object detection, tracking, and environmental modeling.

Capabilities

Camera, radar, lidar data preprocessing
Object detection fusion algorithms
Tracking filter implementation (Kalman, EKF, UKF)
Association algorithms (Hungarian, GNN, JPDA)
Occupancy grid fusion
Confidence estimation and sensor weighting
Time synchronization handling
Ground truth comparison and metrics

Usage Guidelines

Preprocess sensor data for consistent coordinate frames
Select appropriate tracking filters based on object dynamics
Implement robust association for multi-target scenarios
Fuse sensor confidence for reliable perception
Handle time delays and synchronization issues
Validate fusion against ground truth data

Dependencies

ROS/ROS2
TensorFlow
PyTorch
NVIDIA DriveWorks

Process Integration

ADA-001: Perception System Development
ADA-002: Path Planning and Motion Control
ADA-003: ADAS Feature Development
ADA-004: Simulation and Virtual Validation

Links

GitHub Stats

21 forks

Updated 1 month ago

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