Abstract
In this paper, we present a novel navigation framework for the Fetch robot in a large-scale environment based on submapping techniques. This indoor navigation system is divided into a submap mapping part and an on-line localization part. For the mapping part, in order to deal with large environments or multi-story buildings, a submap mapping framework fusing two-dimensional (2D) laser scan and 3D point cloud from RGBD sensor is proposed using Google Cartographer. Meanwhile, several image datasets with corresponding poses are created from the RGBD sensor. Thanks to the submap framework, the error is limited corresponding to the size of the map, thus localization accuracy will be improved. For the on-line localization, so as to switch the submaps, the on-line images from the RGBD sensor are used to match the database images using DeepLCD, a deep learning based library for loop closure. Based on the information from DeepLCD and odometry, adaptive Monte Carlo localization (AMCL) is reinitialized to finish the localization task. In order to validate the result accuracy, reflectors and a motion capture system are used to compute the absolute trajectory error (ATE) and the relative pose error (RPE) based on the Gaussian-Newton (GN) algorithm. Finally, the proposed framework is tested on the Fetch simulator and the real Fetch robot, including both submap mapping and on-line localization.
Highlights
In recent years, indoor navigation has drawn increasing attention in the robotics research area
In order to solve the Simultaneous localization and mapping (SLAM) problem, it is divided into a front-end part, which mainly deals with the input sensor data, and a back-end part involving a non-linear optimization problem to create a consistent map or several submaps [2], [3]
RELATED WORK At the early stage of the SLAM technology, the research topic focuses on the filter-based method, including Extended Kalman Filter (EKF) [18], [19], Particle Filter (PF) [20], Extended Information Filters (EIF) [21] and so on
Summary
Indoor navigation has drawn increasing attention in the robotics research area. Based on the fusion of data from a two-dimensional (2D) laser sensor and an RGB-D camera sensor, this work focuses on the navigation system of the Fetch robot using a deep learning-based SLAM system and multiple submaps. It provides mapping and localization information for the following planning and control system in the relatively large environment. Y. Chen et al.: Submap-Based Indoor Navigation System for the Fetch Robot perform the on-line map switching and localization within multiple submaps. As the state-of-the-art technology in the laser-based SLAM, Cartographer provides the real-time 2D/3D SLAM across multiple platforms and sensor configurations with a very good performance even in some challenging indoor environments.
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