Max Robotics — Global Service Robot Directory + US Certification

About

Dragomir Anguelov is a prominent researcher whose work spans mobile robotics, 3D perception, and autonomous driving, with a career trajectory that moves from foundational probabilistic modeling to cutting-edge deep learning for self-driving systems. His early contributions established robust frameworks for robot environment understanding, including probabilistic door detection from sensor data (2004, 152 citations) and pioneering algorithms for learning hierarchical object maps of dynamic, non-stationary environments (2002, 79 citations) — work that challenged the prevailing assumption of static worlds in robotic mapping. Over time, Anguelov's research evolved toward the demands of autonomous vehicles, producing influential advances in 3D object detection from LiDAR point clouds and range images, including the SWFormer sparse window transformer (2022, 126 citations) and efficient graph-convolution-based detection methods (2021, 71 citations). His more recent work explores neural architecture search for point cloud processing and generative implicit neural representations for scalable simulation environments. Closely associated with Waymo, Anguelov has contributed to open datasets advancing panoptic segmentation research. Across two decades, his cumulative impact reflects a coherent vision: enabling machines to perceive, model, and navigate complex real-world environments with increasing accuracy and efficiency.