Rust for Computer Vision and Robotics: Real-Time Perception, SLAM, and Autonomous Systems with CUDA and ROS2

By Ethan Crossley and Hayden Van Der Post

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 LearnHow to implement efficient computer vision pipelines in Rust using modern libraries and best practicesReal-time object detection, tracking, and image processing with GPU acceleration via CUDABuilding robust SLAM systems for mapping and localization in dynamic environmentsIntegrating Rust with ROS 2 for modular, scalable robotics architecturesWriting safe, concurrent code for perception and control systems in autonomous vehicles, drones, and robotic platformsPerformance optimization techniques for CPU-GPU workflows and real-time constraintsWho 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.

• Format: Paperback
• Language: English
• ISBN: B0H2ZDKFRX
• ISBN13: 9798198674066
• Release Date: May 2026
• Publisher: Independently Published
• Length: 470 Pages
• Weight: 1.24 lbs.
• Dimensions: 1.2" x 6.0" x 9.0"