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SPARKlab

SPARKlab


Sensing, Perception, Autonomy, and Robot Kinetics, cutting edge of robotics and autonomous systems research.

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What we do

✨ The SPARK Lab works at the cutting edge of robotics and autonomous systems research for air, space, and ground applications.

The lab develops the algorithmic foundations of robotics through the innovative design, rigorous analysis, and real-world testing of algorithms for single and multi-robot systems.

A major goal of the lab is to enable human-level perception, world understanding, and navigation on mobile platforms (micro aerial vehicles, self-driving vehicles, ground robots, augmented reality). Core areas of expertise include nonlinear estimation, numerical and distributed optimization, probabilistic inference, graph theory, and computer vision.


News 📰


Research highlights 🔬

Polynomial-time Robust Registration with Extreme Outlier Rates image missing

Polynomial-time Robust Registration with Extreme Outlier Rates

Point cloud registration is a crucial problem in robotics and computer vision, with extensive applications such as object detection and localization and motion estimation and 3D reconstruction. In order to register two point clouds, it is popular to first extract and match feature points to...

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Outlier-Robust Spatial Perception image missing

Outlier-Robust Spatial Perception

Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in...

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Robust Outliers Rejection image missing

Robust Outliers Rejection

The presence of outliers is one of the main causes of failure for simultaneous localization and mapping (SLAM) systems operating in the wild. Perceptual aliasing, in particular, is a phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious...

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