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About

Bharat Joshi is a robotics researcher specializing in underwater autonomy, state estimation, and multi-robot systems, with a particular focus on the unique challenges posed by subaquatic environments. His most influential work, presented at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and garnering over 1,079 citations, systematically evaluated visual-inertial state estimation algorithms in underwater settings — environments largely neglected by mainstream robotics research despite their complexity. This landmark study, complemented by his experimental comparison paper (101 citations), exposed critical gaps between the performance of these algorithms in standard indoor and urban benchmarks versus the demanding realities of underwater operation. Joshi has since developed robust solutions to these challenges, including SM/VIO, a hybrid system that intelligently switches between model-based and visual-inertial odometry to maintain reliable pose estimation even under poor visibility conditions. His research extends to multi-robot coordination, exploring how teams of autonomous underwater vehicles can collaboratively map and explore underwater structures through complementary proximal and distal observation roles. His work on deep learning-based relative localization (DeepURL) further demonstrates his commitment to pushing autonomous underwater capabilities forward. Collectively, Joshi's contributions have meaningfully advanced the field of underwater robotics, establishing him as a leading voice in robust aquatic state estimation and autonomous navigation.