Martin Quigley

Papers

Total Citations

225

H-Index

About

Martin Quigley is a roboticist whose work bridges perception, manipulation, and real-world deployment. His research centers on enabling robots to interact with unstructured environments, particularly through grasping novel objects and achieving robust localization without expensive infrastructure. His most influential contribution, "Grasping novel objects with depth segmentation" (129 citations), demonstrated that cluttered tabletop grasping could be significantly simplified by using depth data to segment objects before applying grasp planners—a pragmatic approach that influenced subsequent work in robotic manipulation. Quigley also advanced indoor localization with "Sub-meter indoor localization in unmodified environments with inexpensive sensors" (57 citations), showing how smartphones' WiFi, cameras, and IMUs could achieve sub-meter accuracy without beacons or maps, opening doors for consumer robotics and augmented reality. In "Low-cost accelerometers for robotic manipulator perception" (39 citations), he proved that consumer-grade accelerometers could estimate 6- and 7-DOF joint angles with two calibration methods, dramatically lowering the cost of robot arm sensing. Quigley’s work is notable for its emphasis on practical, low-cost solutions that make robotic systems more accessible and deployable in everyday settings.