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About

Chunlong Zou is a robotics researcher whose work focuses on autonomous navigation, perception, and localization for mobile robots operating in dynamic, real-world environments. His key research areas include local path planning, simultaneous localization and mapping (SLAM), and deep learning-based scene understanding. Zou’s most impactful contribution is the development of a **Fuzzy Dynamic Window Algorithm** for local path planning, which enhances human-robot collaborative mobile robots by improving obstacle avoidance and trajectory optimization in complex settings—a paper that has garnered **27 citations** since 2023. He also advanced visual SLAM for dynamic scenes by integrating **YOLO-Fastest** object detection, enabling robust real-time localization even when moving objects disrupt traditional static-environment assumptions (14 citations). In 2024, Zou introduced **Ground-LIO**, a LiDAR-inertial odometry method that leverages ground point clouds to significantly boost pose estimation accuracy for ground robots. His work on an **end-to-end instance segmentation method** using an improved ConvNeXt V2 backbone further demonstrates his commitment to enhancing robots’ environmental perception. With a growing citation record and a focus on bridging perception and navigation, Zou is making notable strides toward more intelligent, adaptable autonomous systems.