Zhen Geng

Changchun University of Science and Technology

Papers

Total Citations

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About

Zhen Geng is a researcher at the forefront of visual perception for autonomous systems, with a primary focus on visual odometry and simultaneous localization and mapping (SLAM). His most-cited work, "Visual Odometry Algorithm Based on Deep Learning" (2021), addresses a critical challenge in robotics and autonomous driving: achieving robust, scale-aware relative attitude estimation. By integrating deep learning techniques with traditional geometric methods, Geng’s research enhances the resilience of visual odometry in complex, real-world environments—a key enabler for reliable navigation in self-driving cars and mobile robots. With 6 citations, this paper has already contributed to advancing the field’s understanding of how neural networks can overcome the fragility of conventional approaches. Geng’s work bridges the gap between theoretical SLAM algorithms and practical deployment, tackling issues of poor robustness and scale ambiguity that have long hindered autonomous systems. His contributions are particularly relevant as the demand for safe, perception-driven autonomy continues to grow, making him a promising voice in the intersection of deep learning and robotic vision.