Indoor Mobile Robot Localization and Mapping Based on Ambient Magnetic Fields and Aiding Radio Sources

Abstract

In robotics, the problem of concurrently addressing the localization and mapping is well defined as simultaneous localization and mapping (SLAM) problem. Since the SLAM procedure is usually recursive, maintaining a certain error bound on the current position estimate is a critical issue. However, when the robot is kidnapped (i.e., the robot is moved by an intentional or unintentional user) or suffers from locomotion failure (due to large slip and falling), the robot will inevitably lose its current position. In this case, immediate recovery of the robot position is essential for seamless operation. In this paper, we present a method of solving both SLAM and relocation problems by employing ambient magnetic and radio measurements. The proposed SLAM is realized in the Rao-Blackwellized particle filter-and grid-based SLAM frameworks, where we exploit the local heading corrections from the magnetic measurements. For the relocation, we design the location signatures using the magnetic and radio measurements, and examine each of the Monte Carlo localization-based and multilayer perceptron-based relocation methods with real-world data. We implement the proposed SLAM and relocation algorithms in an embedded system and verify the feasibility of the proposed methods as an online robot navigation system.

A new Wi-Fi dynamic selection of nearest neighbor localization algorithm based on RSS characteristic value extraction by hybrid filtering

“…These technologies are divided into infrastructurebased and infrastructure-free technologies [1]. Infrastructurebased technologies mainly include UWB (ultra-wideband) [2], Bluetooth [3] and RFID (radio frequency identifier) [4] techniques, whereas infrastructure-free technologies typically include Wi-Fi [5,6], magnetic field [7] and PDR (pedestrian dead reckoning) [8,9] techniques. Infrastructure-based technologies require pre-installation facilities and specialized hardware, whereas infrastructure-free technologies require no additional hardware or facilities.…”

Indoor Mapping Guidance Algorithm of Rotary-Wing UAV Including Dead-End Situations

“…Wen et al [15] constructed a three-dimensional indoor mobile mapping system, with data fused by a two-dimensional laser scanner and RGB-D camera. Jung et al [16] provided a method of solving indoor Simultaneous Localization And Mapping (SLAM) and relocation problems by exploiting ambient magnetic and radio sources. Jung et al [17] formulated a feature-based SLAM technique incorporating a constrained least squares method.…”

“…The above studies mainly focused on a mapping system [12,14,18,19,21,22,23,24,25], sensor fusion [13,15,26], or solving a SLAM problem [16,17,20]. Furthermore, mobile robots were mostly considered in the above indoor mapping studies.…”

A novel edge gradient algorithm for multiple mobile robots cooperative mapping in unknown environment

“…1,2 Currently, the simultaneous localization and mapping (SLAM) method is proven to be an effective technique for achieving self-localization and map construction simultaneously, which can be successfully applied to many robotics fields such as soil mapping on farms, 3 monitoring of power lines, 4 and online robot navigation system. 5 In the last decade, many significant approaches in mobile robots localization and mapping have been performed. A fast algorithm of SLAM based on the ball particle filter is presented for mobile robot in the work of Jinwen and Qin, 6 the proposed SLAM algorithm is verified by a series of simulation experiments and exhibited good performance.…”