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About

Abhishek Goudar is a robotics researcher specializing in indoor localization, sensor fusion, and state estimation for mobile and aerial robots. His work centers on ultra-wideband (UWB) technology and its application to scalable, low-cost positioning systems — a domain where reliable public benchmarks and robust algorithms have historically been scarce. Goudar's most impactful contribution is the UTIL dataset (34 citations), a pioneering public benchmark for UWB time-difference-of-arrival indoor localization that has quickly become a community resource for researchers developing and evaluating multi-robot positioning systems. Complementing this, his learning-based bias correction approach for UWB ranging on resource-constrained robots demonstrated how machine learning can meaningfully improve localization accuracy within tight computational budgets. Beyond UWB, Goudar has made significant strides in sensor fusion and range-only estimation. His range-visual-inertial fusion architecture for micro aerial vehicles elegantly combines the complementary strengths of visual-inertial odometry and range sensors, while his work on continuous-time and optimization-based range-only pose estimation addresses fundamental challenges in non-convex trajectory estimation. His exploration of Gaussian variational inference for localization further reflects a commitment to principled probabilistic reasoning under uncertainty. With over 87 cumulative citations, Goudar's research is shaping the foundations of reliable robot navigation in GPS-denied environments.