Apollo

Apollo is a comprehensive software stack designed for autonomous vehicle development, providing the necessary components for perception, planning, and control. It functions as a high-performance robotics middleware, utilizing a publish-subscribe data bus to facilitate low-latency communication between distributed modules and hardware sensors. The platform integrates data from cameras, lidar, and radar through a sensor fusion framework to generate a real-time environmental model for navigation.

The system features a component-based runtime framework that manages task scheduling and resource allocation, supported by a hardware abstraction layer that decouples driving logic from specific vehicle configurations. To ensure consistent behavior during testing, it includes a deterministic replay engine for sensor data streams and supports hardware-in-the-loop simulation. The platform also employs directed acyclic graph scheduling and zero-copy shared memory transport to optimize data flow and computational efficiency across complex robotic systems.

The software provides a standardized vehicle control interface to translate navigation decisions into mechanical commands. Extensive documentation is available, including installation instructions, hardware integration guides, and a series of quick-start manuals for various versions of the platform.

Features

Autonomous Driving Stacks - Provides the necessary components for perception, planning, and control to operate self-driving vehicles safely.

Robotics Middleware - Provides a high-performance communication and task scheduling layer for distributed robotic modules.

Sensor Fusion - Combines inputs from cameras, lidar, and radar sensors to create a reliable real-time environmental model.

Message Buses - Facilitates low-latency communication between distributed autonomous driving modules and hardware sensors.

Motion Planning - Calculates safe and efficient vehicle trajectories by analyzing dynamic obstacles and road conditions.

Sensor Fusion Frameworks - Integrates data from cameras, lidar, and radar to create a unified representation of the surrounding environment.

Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.

Runtime Frameworks - Manages task scheduling and resource allocation for real-time perception and control algorithms.

Autonomous Driving - Open-source autonomous driving platform.

Autonomous Frameworks - Framework for accelerating autonomous vehicle development and deployment.

Calibration Toolboxes - Autonomous driving platform including sensor calibration utilities.

Embedded Utilities - Autonomous driving platform.

Robotics Frameworks - High-performance architecture for autonomous vehicle development.

Hardware-in-the-Loop Simulators - Validates algorithms by connecting software modules to simulated or physical vehicle control systems.

Vehicle Control Interfaces - Translates high-level navigation decisions into precise mechanical commands for steering, braking, and acceleration.

Hardware Abstraction Layers - Decouples high-level driving logic from specific vehicle sensors, actuators, and computing hardware.

Middleware Orchestrators - Manages complex data communication and task scheduling between distributed software components.

Task Schedulers - Executes computational nodes in a specific order based on data dependencies and timing constraints.

Shared Memory Transports - Allows multiple processes to access large sensor data buffers without expensive memory duplication.

Test Infrastructure - Records and replays sensor data streams to ensure consistent behavior during simulation and algorithm testing.

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Features

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