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About

Kshitij Goel is a robotics researcher specializing in autonomous exploration, multi-robot systems, and probabilistic mapping for complex environments. His work sits at the intersection of perception, planning, and communication-efficient coordination, with a particular focus on enabling robot teams to navigate and map large, unstructured three-dimensional spaces. Goel's most influential contribution is his development of Gaussian Mixture Model (GMM)-based frameworks for robotic mapping and exploration. His 2019 paper, "Communication-Efficient Planning and Mapping for Multi-Robot Exploration in Large Environments," has garnered 92 citations and introduced a GMM-based global mapping approach that balances high-fidelity environmental representation with minimal memory overhead — a critical challenge in multi-robot deployments. A companion work from the same year further formalized information-theoretic exploration strategies leveraging GMM compactness, earning 36 citations. Beyond mapping, Goel has advanced fast aerial exploration using multirotors and contributed to subsurface robot exploration, demonstrating the real-world applicability of his methods. His 2023 work on incremental multimodal surface mapping extends his probabilistic modeling philosophy to richer, self-organizing representations. With over 150 cumulative citations, Goel's research has meaningfully shaped how autonomous robots perceive and collaborate in GPS-denied and resource-constrained environments.