Enbao Wang

Beijing Information Science & Technology University

Papers

Total Citations

H-Index

About

Enbao Wang is a researcher advancing the field of robotic perception and autonomous navigation, with a primary focus on Simultaneous Localization and Mapping (SLAM) in dynamic environments. His most cited work, "Improved Visual Odometry Based on SSD Algorithm in Dynamic Environment" (2020), tackles a critical limitation of traditional SLAM systems—their reliance on static surroundings. By integrating the Single Shot Multibox Detector (SSD) deep learning algorithm into visual odometry, Wang developed a robust method that identifies and excludes moving objects, enabling accurate pose estimation even in cluttered, real-world settings. This contribution has garnered 8 citations, reflecting its relevance to researchers addressing SLAM's vulnerability to dynamic interference. Wang’s work bridges computer vision and robotics, offering practical solutions for autonomous vehicles, drones, and service robots operating in unpredictable environments. His research underscores a shift toward adaptive, learning-based approaches in robotic mapping, positioning him as a contributor to making SLAM systems more resilient and deployable outside controlled labs. For students and researchers, Wang’s innovations highlight the importance of integrating object detection with geometric algorithms to overcome real-world challenges in autonomous navigation.