Max Robotics — Global Service Robot Directory + US Certification

About

Rendong Ying is a prominent researcher specializing in autonomous navigation, simultaneous localization and mapping (SLAM), and sensor fusion for robotic and autonomous vehicle systems. His work addresses some of the most pressing challenges in mobile robotics, particularly the seamless integration of heterogeneous sensing modalities to achieve robust, real-world navigation. Ying's most influential contributions include his graph-based adaptive fusion framework for GNSS and visual-inertial odometry (60 citations), which tackles the critical problem of reliable global positioning in GNSS-degraded environments, and his tightly coupled GNSS/V-SLAM integration using 10-DoF manifold optimization (44 citations), which mitigates the drift and scale uncertainty inherent in vision-only systems. His research extends to LiDAR-based feature SLAM using rasterized 3D point clouds (32 citations) and a parallel octree-based nearest neighbor search accelerator for real-time point cloud processing (31 citations), demonstrating a rare combination of algorithmic and hardware-level innovation. Beyond localization, Ying has contributed to semantic traversability mapping, life-long SLAM, and reinforcement learning-based navigation planning, reflecting a holistic systems-level research vision. His development of a reconfigurable multi-sensor testbed further underscores his commitment to translatable, experimentally grounded research. With over 250 cumulative citations, Ying's work meaningfully advances the state of autonomous navigation technology.