Fast and accurate global localization of autonomous ground vehicles is often required in indoor environments and GPS-shaded areas. Typically, with regard to global localization problem, the entire environment should be observed for a long time to converge. To overcome this limitation, a new initialization method called deep initialization is proposed and it is applied to Monte Carlo localization (MCL). The proposed method is based on the combination of a three-dimensional (3D) light detection and ranging (LiDAR) and a camera. Using a camera, pose regression based on a deep convolutional neural network (CNN) is conducted to initialize particles of MCL. Particles are sampled from the tangent space to a manifold structure of the group of rigid motion. Using a 3D LiDAR as a sensor, a particle filter is applied to estimate the sensor pose. Furthermore, we propose a re-localization method for performing initialization whenever a localization failure or the situation of robot kidnapping is detected. Either the localization failure or the kidnapping is detected by combining the outputs from a camera and 3D LiDAR. Finally, the proposed method is applied to a mobile robot platform to prove the method's effectiveness in terms of both the localization accuracy and time consumed for estimating the pose correctly.
Bibliographical noteFunding Information:
This work was supported in part by the Industrial Convergence Core Technology Development Program (Development of Robot Intelligence Technology for Mobility with Learning Capability Toward Robust and Seamless Indoor and Outdoor Autonomous Navigation) funded by the Ministry of Trade, Industry and Energy (MOTIE), South Korea, under Grant 10063172.
© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)