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About

Nicolas Alt is a researcher whose work sits at the intersection of robotics, computer vision, and haptic sensing, with a particular focus on enabling autonomous systems to perceive and interact with complex, unstructured environments. His most significant contribution is in the area of collaborative visual SLAM (Simultaneous Localization and Mapping), where he developed an efficient map compression technique that facilitates information exchange between multiple robots in swarm exploration scenarios—a paper that has garnered 30 citations. Alt has also made notable advances in the challenging problem of reconstructing transparent objects from depth camera data (28 citations), moving beyond the Lambertian assumptions that limit most state-of-the-art methods. A recurring theme in his work is the integration of vision and touch: he has pioneered visuo-haptic sensors that simultaneously capture force measurements and contact shape information, enabling mobile platforms to navigate and manipulate objects with greater scene awareness. His later work on learning-based grasp stability assessment (7 citations) demonstrates a continued commitment to robust, task-oriented robotic manipulation. Through these contributions, Alt has helped push the boundaries of how robots perceive, map, and physically interact with the world around them.