Mixture Density-PoseNet and its Application to Monocular Camera-Based Global Localization

Global localization using a monocular camera is one of the most challenging problems in computer vision and intelligent robotics. In this article, a new deep neural network named Mixture Density (MD)-PoseNet is proposed to address this problem. Unlike existing learning-based global localization methods that return a single guess for the camera pose, MD-PoseNet returns multiple guesses represented in the form of a Gaussian mixture (GM). The key idea of MD-PoseNet is that the network returns the distribution of all probable camera poses instead of the most probable camera pose, and the distribution represents the multiple guesses for the camera pose. The multiple guesses returned by MD-PoseNet are, consequently, exploited in the probabilistic framework of particle filters. Finally, the proposed method is applied to four different environments, and its validity is demonstrated via experiments.

Automated summary

This article introduces a new deep neural network named MD-PoseNet to tackle the challenging problem of global localization using a monocular camera. Unlike existing methods, MD-PoseNet returns multiple guesses for the camera pose, which are then exploited in the probabilistic framework of particle filters.