Abstract
Abstract. Effective and accurate localization method in three-dimensional indoor environments is a key requirement for indoor navigation and lifelong robotic assistance. So far, Monte Carlo Localization (MCL) has given one of the promising solutions for the indoor localization methods. Previous work of MCL has been mostly limited to 2D motion estimation in a planar map, and a few 3D MCL approaches have been recently proposed. However, their localization accuracy and efficiency still remain at an unsatisfactory level (a few hundreds millimetre error at up to a few FPS) or is not fully verified with the precise ground truth. Therefore, the purpose of this study is to improve an accuracy and efficiency of 6DOF motion estimation in 3D MCL for indoor localization. Firstly, a terrestrial laser scanner is used for creating a precise 3D mesh model as an environment map, and a professional-level depth camera is installed as an outer sensor. GPU scene simulation is also introduced to upgrade the speed of prediction phase in MCL. Moreover, for further improvement, GPGPU programming is implemented to realize further speed up of the likelihood estimation phase, and anisotropic particle propagation is introduced into MCL based on the observations from an inertia sensor. Improvements in the localization accuracy and efficiency are verified by the comparison with a previous MCL method. As a result, it was confirmed that GPGPU-based algorithm was effective in increasing the computational efficiency to 10-50 FPS when the number of particles remain below a few hundreds. On the other hand, inertia sensor-based algorithm reduced the localization error to a median of 47mm even with less number of particles. The results showed that our proposed 3D MCL method outperforms the previous one in accuracy and efficiency.
Highlights
With recent interest in indoor navigation and lifelong robotic assistance for human life support, there is an increased need for more effective and accurate localization method in three dimensional indoor environments
Three typical methods have been proposed for the indoor localization; (1) based only on internal sensors such as an odometry or an inertial navigation, (2) utilizing observations of the environment from outer sensors in an a priori or previously learned map such as Monte Carlo Localization (MCL), and (3) relying on infrastructures previously-installed in the environments such as distinct landmarks such as bar-codes, WiFi access points or surveillance camera networks (Borenstein et al, 1997)
Efficiency improvement by GPGPU programming: Figure 5 compares the averaged processing time for single time step of MCL. 6-DOF depth camera pose is estimated in the experiment
Summary
With recent interest in indoor navigation and lifelong robotic assistance for human life support, there is an increased need for more effective and accurate localization method in three dimensional indoor environments. A few 3D MCL approaches have been proposed where rough 3D models and consumer-level depth cameras are used as the environment maps and outer sensors (Fallon et al, 2012; Hornung et al, 2014; Jeong et al, 2013) Their localization accuracy and efficiency still remain at an unsatisfactory level (a few hundreds millimetre error at up to a few FPS) (Fallon et al, 2012; Hornung et al, 2014), or the accuracy is not fully verified using the precise ground truth (Jeong et al, 2013)
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