Machines are being taught to move with purpose, with autonomous vehicles cruising city streets, drones making deliveries, and robots speeding up manufacturing. All of them share one critical need: knowing exactly where they are and where they’re going.
To make this work requires a complex web of satellite signals, sensor data, and human ingenuity, which is where aerospace engineer Shwetabh Singh’s work at the forefront of LIDAR, global navigation satellite systems (GNSS), GPS, and the sensor fusion technologies that pull these technologies together is focused.
Singh’s childhood fascination with fighter jets sparked a career that bridges deep academic research and real-world innovation to solve some of aerospace’s most pressing challenges, from software pipelines that streamline complex aerial data to calibration techniques that improve drone performance in areas where GPS is not available.
The Dreams Before the Drones: Shwetabh Singh’s Early Life
Singh’s passion for flight was born long before he worked in autonomous navigation.
“I would wake up to the thunderous sounds of military cargo planes taking off and landing from the military base near where I grew up,” he recalls. “Instead of being bothered by the noise, I would excitedly run outside to photograph these magnificent machines.”
He was encouraged by his grandfather, a decorated war veteran whose stories fanned the flames of his imagination and curiosity, which led him to study applied physics and mathematical modeling.
During his undergraduate studies, Singh discovered a natural aptitude for experimental physics and decided to pursue a master’s degree in aerospace engineering at Virginia Tech University, where he worked as a graduate research assistant and gained practical experience with integrated sensor payloads for autonomous aerial vehicles.
“Seeing an autonomous drone successfully navigate using the sensor fusion systems we developed was incredibly rewarding,” says Singh. “It felt like a perfect blend of the experimental physics I loved and the aviation technology that had fascinated me since childhood.”
Mapping the Invisible: Inside the Tools that Guide Autonomous Machines
It may seem like magic for the uninitiated, but for engineers like Singh, autonomous navigation means math, sensors, and a careful orchestration of the technologies that help machines understand where they are, even if the usual signals go dark.
In his current role at Inertial Labs, a navigation technology company, Singh’s main focus is on sensor fusion, which merges LIDAR, GNSS, GPS, and inertial measurement units (IMUs) into a single, powerful system which forms the foundation of a more robust, environment-independent system that enables truly autonomous navigation.
Most people are familiar with GPS on a surface level. It offers satellite-based location data, developed specifically by and for the United States to help anything from smartphones to aircraft navigate. Its global counterpart, GNSS, expands that same concept from the U.S. to the rest of the world.
LIDAR is a remote sensing technology that uses laser pulses to map the physical environment in 3D for real-time analysis, and IMUs track the motion and orientation of a moving object from the inside, helping to measure the object’s movement and velocity without relying on external GPS or GNSS data.
When put together in one coherent system you get sensor fusion, which provides a means of navigating that is as precise as it is comprehensive.
Real-World Applications and Industry Impact Through RESEPI
Another key part of Singh’s work at Inertial Labs is the implementation of sensor fusion on RESEPI, a compact sensor suite that builds high-precision 3D maps and geospatial data. By taking its input from GPS, GNSS, IMUs, and LIDAR, it becomes a functional navigational system that drones and other autonomous vehicles can use to navigate.
He helped to develop simultaneous localization and mapping (SLAM) and visual odometry, which integrates LIDAR, IMU, and camera systems, ensuring robust system performance even in GPS-denied environments like urban canyons and underground structures.
Thanks to Singh’s work, RESEPI can improve both autonomous navigation and the precision of infrastructure mapping.
He also played a key role in the calibration and procedures that improved the accuracy and reliability of RESEPI, and streamlined the software pipeline to speed up data processing without sacrificing accuracy. He even implemented testing protocols across hardware, firmware, and software that provide real-time quality assurance of its data and outputs, which is crucial in an area where a single missed or faulty datapoint can lead to a crash or inaccurate maps.
But it was the flight tests with small drones that truly shaped the system. Singh gathered real-world data from each mission, which allowed him to validate his models and push the system’s capabilities even further.
These insights not only improved the product but also helped to define its future, ensuring Inertial Labs stays competitive in a rapidly evolving field.
From Childhood Curiosity to the Future of Autonomous Navigation
In the future, Shwetabh Singh hopes to achieve truly autonomous navigation in all types of terrain, similar to Tesla’s innovation in the ground-based autonomous vehicle space. He’d also like to contribute to edge robotics, focusing on real-time sensing and decision-making capabilities that can happen in the fractions of a second, even in crowded urban environments.
Driving his vision is a continued focus on bridging academic research with real-world solutions. After all, pushing the limits of precision mapping and navigation is only possible by advancing the theory of sensor fusion with an eye on its practical applications.
But at its foundation, Singh’s drive is based on his childhood memories. He recalls a moment near the airbase, where he would take his aircraft photographs. One morning, as a massive cargo plane took off overhead, he found himself sketching diagrams, trying to understand how something so heavy could fly with such precision.
Years later, he’s still chasing that question; only now, his tools are far more sophisticated.