Smart alignment is a fundamental requirement for charging in the Internet of Things (IoT). The pose or relative pose is crucial for achieving this smart alignment. This paper addresses the deployment problem for anchors on the detector and tags on an object, leveraging range measurements for high accuracy. We designed a Reference Point Transformation (RPT) joint deployment algorithm, including a topology framework of deployment, a joint deployment model, and the RPT module in 6 Degrees of Freedom (6-DoF). First, the topology framework of deployment decreases the search space of the optimization problem. Second, the joint deployment model represents anchors and tags together mathematically, allowing the deployment problem to be treated in a unified framework. The final component of our framework aims to determine an optimal anchor/tag configuration at a designated target point. Specifically, using the translation and rotation between the reference and target points, the optimization criterion shifts from the Geometric Dilution of Precision (GDOP) at the reference point to the square root of the trace of the Cramér-Rao Lower Bound (CRLB) for the target point position. Thereafter, simulations and experiments were conducted to demonstrate the rationality of the RPT deployment algorithm compared with three other deployment algorithms. The good performance of the RPT deployment algorithm verifies that it is suitable for 6-DoF Rigid Body Localization (RBL). Finally, the extended performance analysis of the RPT deployment algorithm is conducted to illustrate two relationships related to the lower bound of localization errors. One is the detector dimensions under the topology framework. The other is the number of anchors and tags under the RPT deployment algorithm.

Accurate indoor pathloss prediction is crucial for optimizing wireless communication in indoor settings, where diverse materials and complex electromagnetic interactions pose significant modeling challenges. This paper introduces TransPathNet, a novel two-stage deep learning framework that leverages transformer-based feature extraction and multiscale convolutional attention decoding to generate high-precision indoor radio pathloss maps. TransPathNet demonstrates state-of-the-art performance in the ICASSP 2025 Indoor Pathloss Radio Map Prediction Challenge, achieving an overall Root Mean Squared Error (RMSE) of 10.397 dB on the challenge full test set and 9.73 dB on the challenge Kaggle test set, showing excellent generalization capabilities across different indoor geometries, frequencies, and antenna patterns. Our project page, including the associated code, is available at https://lixin.ai/TransPathNet/.

To perform target-following tasks in unknown environments, a robot must identify the target’s position and plan an efficient path to reach it. Traditional LiDAR-based localization systems face challenges in distinguishing the target from objects with similar appearances. Meanwhile, existing target-following approaches often neglect target visibility during path planning, leading to target occlusion by obstacles and ultimately resulting in following failure. In this paper, we propose a sequence matching method for target-localization using LiDAR and Ultra-Wideband (UWB) ranging. We determine the position of the target by analyzing the similarities between UWB ranging sequence and LiDAR cluster trajectories. To achieve visibility-aware target-following, we incorporate a visibility objective function into the Dynamic Window Approach (DWA) to generate a following path that minimizes the risk of target loss. This function evaluates the target loss risk based on the positional relationships between the robot, the target, and the nearest obstacle to the target. Extensive experiments were conducted using both human and robot as targets. The results show that our approach achieves higher completion rates when compared to the target-following using traditional DWA.

The Position information is essential for large-scale Internet of Things (IoT) devices and services. Multipath and non-line of sight (NLOS) effects introduce additional delays in pseudorange measurements in urban areas. It is one of the main unmodeled errors in Global Navigation Satellite Systems (GNSS). To mitigate interference, various techniques have been developed, including antenna design and sensor fusion. However, traditional estimation approaches often produce biased estimates under the additional path delays. To improve estimation accuracy and robustness, we present an Observation Space Representation Refinement (OSRR) algorithm. The initial position is estimated by least squares without the additional path error. Then, the multipath projection method is used to get possible compensation in pseudorange measurements. Subsequently, the Moving Horizontal Estimation (MHE) is leveraged to get the position with corrected observation space. Field experiments demonstrate that the proposed OSRR algorithm significantly reduces the impact of interference on positioning accuracy. There is no empirical constraint to easily adapt to real-time static and kinematic GNSS pseudorange positioning with unilateral obstruction scenarios.

Collaborative multiple robots for unknown environment exploration have become mainstream due to their remarkable performance and efficiency. However, most existing methods assume perfect robots’ communication during exploration, which is unattainable in real-world settings. Though there have been recent works aiming to tackle communication-constrained situations, substantial room for advancement remains for both information-sharing and exploration strategy aspects. In this paper, we propose a Communication-Constrained Multi-Robot Entropy-Field-Based Exploration (MEF-Explore). The first module of the proposed method is the two-layer inter-robot communication-aware information-sharing strategy. A dynamic graph is used to represent a multi-robot network and to determine communication based on whether it is low-speed or high-speed. Specifically, low-speed communication, which is always accessible between every robot, can only be used to share their current positions. If robots are within a certain range, high-speed communication will be available for inter-robot map merging. The second module is the entropy-field-based exploration strategy. Particularly, robots explore the unknown area distributedly according to the novel forms constructed to evaluate the entropies of frontiers and robots. These entropies can also trigger implicit robot rendezvous to enhance inter-robot map merging if feasible. In addition, we include the duration-adaptive goal-assigning module to manage robots’ goal assignment. The simulation results demonstrate that our MEF-Explore surpasses the existing ones regarding exploration time and success rate in all scenarios. For real-world experiments, our method leads to a 21.32% faster exploration time and a 16.67% higher success rate compared to the baseline.

Odometry is commonly used in localization applications especially with wheeled platforms since encoders are readily available. It is often used by itself or fused with other sensor data to obtain a better estimate. However, its limitation is its exclusivity to wheeled platforms whereas it is often desired to have similar encoder odometry options on other systems. Given that WiFi is ubiquitous in most commercial and industrial areas, in this paper, a method is proposed for obtaining odometry from WiFi scans for position estimation. The method is not constrained to wheel robots such as the case for wheeled odometry and does not rely on the traditional fingerprinting method. The proposed method involves training a neural network model to predict the distance moved based on features extracted from WiFi scans in the environment. These distances moved are then summed up to obtain the trajectory. Experiments are conducted and the methods are evaluated based on Root Mean Square Error (RMSE). Experimental results showed that the proposed method is able to achieve an RMSE of at most 8.39m for the various test cases.Note to Practitioners—This paper was motivated by the limited sensors available for odometry. Existing methods of odometry either require a wheeled platform or exteroceptive sensors to be placed outside of the robot so that it can see the environment. This paper proposes a new and low-cost method of performing odometry using a WiFi receiver and Inertial Measurement Unit (IMU) with a neural network model. This provides an alternative that exploits existing WiFi infrastructure and thus more flexibility in robot design without wheels and sensor placement constraints. We show how the features are selected as well as propose several similarity methods to choose from. We then show how the neural network model is trained and used during implementation. Preliminary physical experiments suggest that the method was able to obtain the trajectory of a robot in two different environments using the same model and different speeds.

Determining the location of Wi-Fi access points (APs) is vital for various Wi-Fi-based applications, such as localization, security, and AP deployment. Considerable effort has been exerted in the field of AP localization. In contrast to studies that require additional robots with specialized antenna arrays, we present EasyAPPos, a lightweight, always-on, and user-centered AP positioning solution that utilizes widely available mobile phones. We focus on addressing three challenges in AP positioning. First, the patch antenna on a mobile phone has a limited angular range due to its size, but our approach proposes a method for utilizing human natural rotation to enhance angular diversity. Second, our angle-based algorithm does not require synchronous clocks between the mobile device and the APs, in contrast to existing algorithms that require this synchrony to transform propagation delays into positions. Nevertheless, our algorithm can still utilize asynchronous delay information. Third, the low bandwidth of Wi-Fi beacon frames, which only provide limited capacity to counteract the effects of multipath, is addressed by performing AP positioning under challenging conditions. We validate EasyAPPos through simulations and experiments, which demonstrate its ability to achieve decimeter-level positioning accuracy even under harsh conditions.

To accomplish task efficiently in a multiple robots system, a problem that has to be addressed is Simultaneous Localization and Mapping (SLAM). LiDAR (Light Detection and Ranging) has been used for many SLAM solutions due to its superb accuracy, but its performance degrades in featureless environments, like tunnels or long corridors. Centralized SLAM solves the problem with a cloud server, which requires a huge amount of computational resources and lacks robustness against central node failure. To address these issues, we present a distributed SLAM solution to estimate the trajectory of a group of robots using Ultra-WideBand (UWB) ranging and odometry measurements. The proposed approach distributes the processing among the robot team and significantly mitigates the computation concern emerged from the centralized SLAM. Our solution determines the relative pose (also known as loop closure) between two robots by minimizing the UWB ranging measurements taken at different positions when the robots are in close proximity. UWB provides a good distance measure in line-of-sight conditions, but retrieving a precise pose estimation remains a challenge, due to ranging noise and unpredictable path traveled by the robot. To deal with the suspicious loop closures, we use Pairwise Consistency Maximization (PCM) to examine the quality of loop closures and perform outlier rejections. The filtered loop closures are then fused with odometry in a distributed pose graph optimization (DPGO) module to recover the full trajectory of the robot team. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

Relative localization between autonomous robots without infrastructure is crucial to achieve their navigation, path planning, and formation in many applications, such as emergency response, where acquiring a prior knowledge of the environment is not possible. The traditional Ultra-WideBand (UWB)-based approach provides a good estimation of the distance between the robots, but obtaining the relative pose (including the displacement and orientation) remains challenging. We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes. We determine the pose between two robots by minimizing the residual error of the ranging measurements from all UWB nodes. To improve the localization accuracy, we propose to utilize the odometry constraints through a sliding window-based optimization. The optimized pose is then fused with the odometry in a particle filtering for pose tracking among a group of mobile robots. We have conducted extensive experiments to validate the effectiveness of the proposed approach.

Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of the Wi-Fi wireless network. This article presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m with a step counter-based PDR.

Relative positioning between multiple mobile users is essential for many applications, such as search and rescue in disaster areas or human social interaction. Inertial-measurement unit (IMU) is promising to determine the change of position over short periods of time, but it is very sensitive to error accumulation over long term run. By equipping the mobile users with ranging unit, e.g. ultra-wideband (UWB), it is possible to achieve accurate relative positioning by trilateration-based approaches. As compared to vision or laser-based sensors, the UWB does not need to be with in line-of-sight and provides accurate distance estimation. However, UWB does not provide any bearing information and the communication range is limited, thus UWB alone cannot determine the user location without any ambiguity. In this paper, we propose an approach to combine IMU inertial and UWB ranging measurement for relative positioning between multiple mobile users without the knowledge of the infrastructure. We incorporate the UWB and the IMU measurement into a probabilistic-based framework, which allows to cooperatively position a group of mobile users and recover from positioning failures. We have conducted extensive experiments to demonstrate the benefits of incorporating IMU inertial and UWB ranging measurements.

In this paper, we propose an indoor localization system employing ordered sequence of access points (APs) based on received signal strength (RSS). Unlike existing indoor localization systems, our approach does not require any time-consuming and laborious site survey phase to characterize the radio signals in the environment. To be precise, we construct the fingerprint map by cutting the layouts of the interested area into regions with only the knowledge of positions of APs. This can be done offline within a second and has a potential for practical use. The localization is then achieved by matching the ordered AP-sequence to the ones in the fingerprint map. Different from traditional fingerprinting that employing all APs information, we use only selected APs to perform localization, due to the fact that, without site survey, the possibility in obtaining the correct AP sequence is lower if it involves more APs. Experimental results show that, the proposed system achieves localization accuracy < 5m with an accumulative density function (CDF) of 50% to 60% depending on the density of APs. Furthermore, we observe that, using all APs for localization might not achieve the best localization accuracy, e.g. in our case, 4 APs out of total 7 APs achieves the best performance. In practice, the number of APs used to perform localization should be a design parameter based on the placement of APs.

Recent research deals more and more with the application of ultra high frequency (UHF) radio-frequency identification (RFID) on mobile robots. However, the sensing characteristics between the reader and the tag (i.e. detections and signal strength) are challenging to model due to the influence of environmental effects (e.g. tag density, reflection, diffraction, or absorption). In this paper, we address the problem of dynamic objects tracking with a mobile agent using the signal strength from UHF RFID tags attached to objects. Our solution estimates the positions of RFID tags under a Bayesian framework. More precisely, we combine a two stage dynamic motion model with the dual particle filter, to capture the dynamic motion of the object and to quickly recover from failures in tracking. This approach is then tested on a Scitos G5 mobile robot through various experiments.

Recently, researchers showed growing interest in utilizing UHF Radio-Frequency Identification (RFID) technology for localizing tagged items with mobile robots in industrial scenarios. In this paper we present a novel three-dimensional (3D) probability sensor model of RFID antennas in the context of mapping passive RFID tags with mobile robots. The proposed 3D sensor model characterizes both detection rates and received signal strength (RSS). Compared to 2D-sensor model based approaches, the 3D model gains a higher mapping accuracy for 2D position estimation. Specially, with this sensor model, we are able to localize the tags in 3D by integrating the measurements from a pair of RFID antennas mounted at different heights of the robot. Furthermore, by integrating negative information (i.e., non-detections), the 3D mapping accuracy can be improved. Additionally, we utilize KLD-sampling to reduce the number of particles for our specific application, so that our algorithm can be performed online. Indoor experiments with a Scitos G5 robot demonstrate the effectiveness of our approach. We also provide the datasets of this work for download.

We present a novel approach incorporating a combination of Radio-Frequency Identification (RFID) and odometry information into the motion control of a mobile robot for the purpose of path following in unknown environments. Our method utilizes RFID measurements as landmarks and makes the mobile robot autonomously follow a path that was previously recorded in a manual training phase. The approach needs no prior information about RFID sensor models, the distribution and positioning of the tags nor does it require a map of the environment. Particularly, it is adaptive to different reader power levels and various tag densities, which have a major impact on RFID performance. Extensive experiments with a SCITOS G5 robot in different environments like a library, a supermarket and hallways confirm the effectiveness of our algorithm.