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Book synopsis

Reactive Publishing

Build high-performance, safe, and reliable computer vision and robotics applications using Rust.

This book provides a practical, in-depth guide to developing real-time perception systems, Simultaneous Localization and Mapping (SLAM), and autonomous robotics applications with Rust. By combining Rust's memory safety and performance with CUDA acceleration and ROS 2, you'll learn how to create production-ready solutions that avoid the common pitfalls of C++ while maintaining maximum speed.

What You'll Learn

• How to implement efficient computer vision pipelines in Rust using modern libraries and best practices
• Real-time object detection, tracking, and image processing with GPU acceleration via CUDA
• Building robust SLAM systems for mapping and localization in dynamic environments
• Integrating Rust with ROS 2 for modular, scalable robotics architectures
• Writing safe, concurrent code for perception and control systems in autonomous vehicles, drones, and robotic platforms
• Performance optimization techniques for CPU-GPU workflows and real-time constraints

Who This Book Is For

Ideal for intermediate to advanced Rust developers, robotics engineers, computer vision practitioners, and researchers who want to leverage Rust's strengths in safety-critical and performance-intensive domains. Whether you're transitioning from C++ or looking to modernize existing ROS-based systems, this book offers clear, code-heavy examples and architectural guidance.

Focused on practical implementation rather than theory, Rust for Computer Vision and Robotics bridges the gap between high-performance computing and memory-safe systems programming. All examples are built with current, actively maintained tools and libraries.

Book details

• Full title: Rust for Computer Vision and Robotics
• Subtitle: Real-Time Perception, SLAM, and Autonomous Systems with CUDA and ROS2
• Author: Hayden Van Der Post, Ethan Crossley, Danny Munrow
• Language: English
• Binding: Paperback
• Number of pages: 470
• EAN: 9798198674066
• ID: 52537625
• Publisher: Independently published
• Weight: 564 g
• Dimensions: 229 × 152 × 30 mm
• Date of publishing: 26. May 2026

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