Shival Indermun
Papers
2
Total Citations
2
H-Index
1
About
Shival Indermun is a robotics researcher advancing safe and intelligent autonomous navigation in dynamic, real-world environments. His work centers on two critical challenges: risk-aware path planning and robust visual SLAM in the presence of moving objects. In his 2024 paper on risk-aware navigation, Indermun introduced "Safety Classes Semantic Costmaps," which enrich traditional 2D cost maps with semantic information from RGBD sensors. This innovation allows robots to move beyond arbitrary safety margins, enabling context-aware obstacle avoidance that adapts to the nature of detected objects—a significant step toward safer human-robot interaction. Complementing this, his research on dynamic human–object interaction detection tackles a persistent weakness in visual SLAM systems like ORBSLAM3. By identifying and excluding features from moving entities, his method dramatically improves localization accuracy in cluttered, dynamic scenes. Though early in his career, with each paper already garnering initial citations, Indermun’s work directly addresses fundamental gaps in autonomous robotics. His contributions are particularly relevant for service robots, autonomous vehicles, and any system that must operate safely alongside humans, marking him as a promising voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
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