Saad Mokssit
Papers
1
Total Citations
71
H-Index
1
About
Saad Mokssit is a leading researcher at the intersection of computer vision and robotics, with a primary focus on Visual Simultaneous Localization and Mapping (VSLAM). His most-cited work, the 2023 survey "Deep Learning Techniques for Visual SLAM: A Survey," has already garnered 71 citations, establishing him as a key voice in the field. In this seminal paper, Mokssit critically examines how deep learning is revolutionizing traditional VSLAM—a task that enables robots to localize themselves and map unknown environments using visual sensors. He highlights the shift away from labor-intensive handcrafted feature extraction toward end-to-end learned models, addressing persistent challenges in robustness, scalability, and real-time performance. Beyond this survey, Mokssit’s research continues to push the boundaries of autonomous navigation, aiming to make intelligent systems more adaptive and reliable in dynamic, unstructured settings. His work is essential reading for students and researchers seeking to understand the current state and future trajectory of deep learning in robotic perception and mapping.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning Techniques for Visual SLAM: A Survey71 citations · 2023