Max Robotics — Global Service Robot Directory + US Certification

About

Kurran Singh is a robotics researcher whose work lies at the intersection of state estimation, perception, and environmental reasoning. His most impactful contribution, the highly cited "Discrete-Continuous Smoothing and Mapping" (2022), introduces a general framework for maximum *a posteriori* (MAP) inference in hybrid discrete-continuous systems, a foundational advance for robust simultaneous localization and mapping (SLAM) in complex environments. Singh also made significant strides in long-term autonomy with his work on "Robust Change Detection Based on Neural Descriptor Fields" (2022), which enables robots to reliably identify environmental changes despite varying viewpoints and accumulated sensor noise—a critical capability for persistent operation. Extending his expertise to challenging underwater domains, he developed a novel "Hybrid Long/Inverted Ultra-Short Baseline (LBL-iUSBL) Acoustic Pose Estimation" system (2025), providing precise 6-DOF pose estimation for underwater sonar mapping. With over 29 citations across his key publications, Singh is recognized for bridging theoretical inference methods with practical, real-world robotic systems, particularly in environments where discrete changes and continuous dynamics must be jointly understood.