Plane-extraction from depth-data using a Gaussian mixture regression model

TL;DR

This paper introduces an unsupervised, probabilistic method for extracting piecewise planar models from depth data, improving accuracy and robustness for autonomous perception and navigation.

Contribution

It presents a novel Gaussian mixture regression model with skewed components and outlier trimming, enhancing plane extraction from depth data compared to existing methods.

Findings

Ranks among the best in standard benchmarks

Effectively fuses coplanar components for better accuracy

Robustly handles outliers in depth data

Abstract

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.