Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification

Abstract

The millimeter wave (mmWave) bands have attracted considerable attention for high precision localization applications due to the ability to capture high angular and temporal resolution measurements. This paper explores mmWave-based positioning for a target localization problem where a fixed target broadcasts mmWave signals and a mobile robotic agent attempts to capture the signals to locate and navigate to the target. A three-stage procedure is proposed: First, the mobile agent uses tensor decomposition methods to detect the multipath channel components and estimate their parameters. Second, a machine-learning trained classifier is then used to predict the link state, meaning if the strongest path is line-of-sight (LOS) or non-LOS (NLOS). For the NLOS case, the link state predictor also determines if the strongest path arrived via one or more reflections. Third, based on the link state, the agent either follows the estimated angles or uses computer vision or other sensor to explore and map the environment. The method is demonstrated on a large dataset of indoor environments supplemented with ray tracing to simulate the wireless propagation. The path estimation and link state classification are also integrated into a state-of-the-art neural simultaneous localization and mapping (SLAM) module to augment camera and LIDAR-based navigation. It is shown that the link state classifier can successfully generalize to completely new environments outside the training set. In addition, the neural-SLAM module with the wireless path estimation and link state classifier provides rapid navigation to the target, close to a baseline that knows the target location.

Radio SLAM: A Review on Radio-Based Simultaneous Localization and Mapping

“…However, these image-based datasets are mainly aimed toward the implementation of visual SLAM using computer vision algorithms since cameras are the primary sensors in this case. Nevertheless, a recent work [117] has integrated wireless propagation information with the help of wireless channel simulators, such as Remcom [118], within the Gibson dataset [119]. They validated the simulations on AI Habitat [116] and have released an augmented Gibson dataset [120] that also contains wireless ray tracing data along with the camera and LiDAR data.…”

Wireless Channel Prediction in Partially Observed Environments

“…The methods are validated on a realistic simulation of robot navigation and visual SLAM map construction, used in prior work [17]. We used the Gibson indoor dataset which contains a collection of accurate 3D maps along with camera data [10].…”

“…We performed ray tracing simulations using Remcom Wireless InSite [11] at 28 GHz to obtain ground truth values for the channels in each TX-RX link. Then, similar to [17], we use the Active Neural SLAM algorithm [9] to simulate robot indoor exploration on the AI Habitat platform from visual information [18]. The robot explores the environment and gradually builds a simplified 3D map.…”

Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping

“…A widely recognized vision for the next generation wireless communication system, such as beyond 5G (B5G) or 6G networks, is to be combined with sensing systems, realizing efficient utilization wireless resources, wide environment sensing functions, and even pursuing mutual benefits [1], [2]. Therefore, integrated sensing and communication (ISAC) is considered as one of the most important and promising technologies in the future communication systems and has attracted increasingly research interest recently [3], [4], [19], [26]. Many ISAC technologies have investigated how to improve the efficiency and quality of data transmissions in wireless communication using sensing functions [5], [6].…”

“…Traditional SLAM algorithms usually leverage LIDAR [7] or computer vision methods [8]. Nonetheless, as a typical ISAC technique that enables sensing functions only with wireless signals, communication-based SLAM (C-SLAM) has been studied in some recent literatures [3], [9]- [13], [19]. Specifically, in terms of mapping, some researchers propose belief propagation (BP)-based SLAM algorithms to detect the physical anchors (PAs) and virtual anchors (VAs) that can represent specific boundaries in simple indoor environments [3], [9]- [11].…”