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.
Bibliographical noteFunding Information:
Manuscript received December 7, 2019; revised February 15, 2020; accepted March 18, 2020. Date of publication April 8, 2020; date of current version October 23, 2020. This work was supported by the Industry Core Technology Development Project, 20005062, Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space, funded by the Ministry of Trade, Industry & Energy (MOTIE, South Korea). Paper no. TII-19-5210. (Corresponding author: Euntai Kim.) HyungGi Jo and Euntai Kim are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: email@example.com; firstname.lastname@example.org).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering