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About

Ben Bellekens is a researcher whose work sits at the intersection of robotics, computer vision, and wireless communication, with a particular focus on 3D spatial perception and its real-world applications. His most influential contribution is the comprehensive survey on rigid 3D pointcloud registration algorithms (97 citations), which has become a foundational reference for researchers working on geometric alignment using depth sensors—a critical task for autonomous navigation and mapping. Bellekens also developed the 3DVFH+ algorithm (25 citations), a real-time method for three-dimensional obstacle avoidance that leverages OctoMap representations, enabling robots to navigate complex 3D environments more efficiently. In a novel interdisciplinary direction, he validated an indoor ray-launching radio frequency propagation model that uses SLAM-generated environment models to predict sub-GHz signal behavior. This work, further expanded in his 2018 article on realistic indoor radio propagation, directly addresses the growing demands of the Internet of Things by bridging robotic mapping with wireless network planning. Bellekens’s research demonstrates a unique ability to connect spatial mapping algorithms with practical engineering challenges, making his work valuable for both roboticists and communications engineers.