Max Calcroft
Papers
1
Total Citations
5
H-Index
1
About
Max Calcroft is an emerging researcher in autonomous systems and embedded sensing, with a focus on practical, low-cost implementations for self-driving technologies. His most cited work, "LiDAR-based Obstacle Detection and Avoidance for Autonomous Vehicles using Raspberry Pi 3B" (2022), demonstrates a scalable approach to integrating Hokuyo URG-04LX LiDAR sensors with accessible single-board computers. This research addresses a critical bottleneck in autonomous vehicle development—reliable obstacle detection and avoidance—by proving that high-performance sensing can be achieved without expensive, proprietary hardware. With 5 citations, this paper has already informed subsequent studies in cost-effective robotics and embedded perception systems. Calcroft’s contributions lie at the intersection of sensor fusion, real-time processing, and open-source hardware, offering a blueprint for democratizing autonomous vehicle research. His work is particularly valuable for students and engineers seeking to prototype advanced driver-assistance systems (ADAS) on limited budgets, bridging the gap between theoretical algorithms and deployable, real-world solutions.
Research Focus
Key Achievements
Top Papers
- 1