Zengke Li

China University of Mining and Technology

Papers

Total Citations

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About

Zengke Li is a researcher at the forefront of autonomous navigation and sensor fusion, with a primary focus on advancing Simultaneous Localization and Mapping (SLAM) technology for mobile robots. His major contributions lie in developing robust, multi-sensor systems that overcome the limitations of individual sensors in challenging environments. Notably, his work on the BVLI-SLAM scheme, which fuses binocular vision, 2D lidar, and IMU data, has garnered 22 citations for enabling reliable outdoor localization and indoor planar mapping. Li’s most cited paper (26 citations) addresses the critical issue of initial scale ambiguity in visual-inertial navigation systems, demonstrating robust performance even with low-precision sensors across both indoor and outdoor settings. More recently, he has tackled the persistent problem of non-line-of-sight (NLOS) errors in Ultra-Wideband (UWB) indoor positioning, proposing a novel error-reduction method that strengthens the accuracy of indoor positioning systems. Through these contributions, Li has established himself as a key innovator in creating resilient, integrated navigation solutions that push the boundaries of autonomous robotics and IoT applications.