Plane-extraction from depth-data using a Gaussian mixture regression model
This work addresses the need for accurate plane extraction to enable autonomous agents like robots and drones to perceive and navigate in 3D environments, representing an incremental improvement over existing methods.
The paper tackles the problem of unsupervised extraction of piecewise planar models from depth data, proposing a Gaussian mixture regression model with skewed components and outlier trimming, and shows it ranks among the best state-of-the-art methods on a standard benchmark.
We propose a novel algorithm for unsupervised extraction of piecewise planar models from depth-data. Among other applications, such models are a good way of enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive their surroundings and to navigate in three dimensions. We propose to do this by fitting the data with a piecewise-linear Gaussian mixture regression model whose components are skewed over planes, making them flat in appearance rather than being ellipsoidal, by embedding an outlier-trimming process that is formally incorporated into the proposed expectation-maximization algorithm, and by selectively fusing contiguous, coplanar components. Part of our motivation is an attempt to estimate more accurate plane-extraction by allowing each model component to make use of all available data through probabilistic clustering. The algorithm is thoroughly evaluated against a standard benchmark and is shown to rank among the best of the existing state-of-the-art methods.