Max Robotics — Global Service Robot Directory + US Certification

About

Wenlu Yu is a robotics researcher whose work focuses on solving critical perception and calibration challenges in autonomous systems, particularly for LiDAR and multi-camera setups. Their major contributions lie in developing robust extrinsic calibration methods for sensors with non-overlapping fields of view—a common problem in real-world robotics. Yu’s 2022 paper "CamMap" introduced a novel approach using SLAM map alignment to calibrate non-overlapping cameras, earning 16 citations for addressing a key bottleneck in multi-sensor fusion. Building on this, "LiDAR-Link" (2024, 7 citations) extended the concept to solid-state LiDARs with probabilistic, observability-aware plane-based calibration. Yu also advanced SLAM in challenging environments with "MM-LINS" (2024, 11 citations), a multi-map LiDAR-inertial system designed to handle over-degenerate conditions like crowds and sensor occlusion. Their work "I²EKF-LO" (2024, 10 citations) further pushed LiDAR odometry by improving the Iterative Extended Kalman Filter framework. With over 44 citations across these key papers, Yu’s research is directly applicable to warehouse logistics, healthcare robotics, and autonomous driving, demonstrating a clear impact on making robots more reliable in complex, real-world settings.