Vincentius Charles Maynad
Papers
1
Total Citations
1
H-Index
1
About
Vincentius Charles Maynad is a robotics researcher specializing in autonomous navigation, 3D perception, and intelligent locomotion systems. His work focuses on enhancing mobile robot autonomy in complex indoor environments through innovative sensor fusion and path planning techniques. His most cited paper, "Dynamic Mapping and 3D Perception Using Voxel Grid and Modified Artificial Potential Fields for Indoor Locomotion" (2025), introduces a novel integration of RTAB-Map with Voxel Grid Filters and Joint Probabilistic Data Association (JPDA) for efficient environmental mapping. The study further advances local path planning by combining pure pursuit with modified artificial potential fields, enabling robust obstacle avoidance and smooth trajectory generation. This work demonstrates Maynad's ability to bridge theoretical algorithms with practical robotic applications, achieving real-time 3D perception and dynamic navigation. With 1 citation to date, his research contributes to the growing field of indoor robotics, offering scalable solutions for autonomous systems in warehouses, hospitals, and smart buildings. Maynad’s interdisciplinary approach—merging computer vision, control theory, and probabilistic data association—positions him as an emerging voice in intelligent locomotion and mapping technologies.
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Top Papers
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