Anamitra Makur

About

Anamitra Makur is a leading researcher in autonomous robotics and intelligent systems, with a primary focus on active simultaneous localization and mapping (SLAM) and deep reinforcement learning for large-scale environments. His most impactful work, "LiDAR-Based End-to-End Active SLAM Using Deep Reinforcement Learning in Large-Scale Environments" (2024, 14 citations), introduces a groundbreaking approach to autonomous exploration that addresses the critical computational bottlenecks faced by mobile platforms in expansive, complex spaces. By developing an end-to-end deep reinforcement learning framework, Makur enables robots to make intelligent, real-time navigation decisions without relying on traditional, computationally expensive mapping pipelines. This innovation significantly reduces overhead, allowing for efficient and scalable exploration in environments where conventional algorithms fail. His contributions bridge the gap between theoretical reinforcement learning and practical robotic deployment, offering a robust solution for applications ranging from search-and-rescue to planetary exploration. Makur’s work is notable for its direct impact on advancing autonomous navigation capabilities, and his research continues to shape the future of intelligent, self-guided robotic systems in challenging, real-world settings.

Research Focus

Key Achievements

Top Papers

1. 1
   LiDAR-Based End-to-End Active SLAM Using Deep Reinforcement Learning in Large-Scale Environments

   14 citations · 2024

Key Collaborators

Contact & Links