Implementation of a Vector-based Tracking Loop Receiver in a Pseudolite Navigation System

We propose a vector tracking loop (VTL) algorithm for an asynchronous pseudolite navigation system. It was implemented in a software receiver and experiments in an indoor navigation system were conducted. Test results show that the VTL successfully tracks signals against the near–far problem, one of the major limitations in pseudolite navigation systems, and could improve positioning availability by extending pseudolite navigation coverage

Localization and Navigation System for Indoor Mobile Robot

Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.Comment: Accepted by the 2023 5th International Conference on Materials Science, Machine and Energy Engineerin

Enhancing the map usage for indoor location-aware systems

Location-aware systems are receiving more and more interest in both academia and industry due to their promising prospective in a broad category of so-called Location-Based-Services (LBS). The map interface plays a crucial role in the location-aware systems, especially for indoor scenarios. This paper addresses the usage of map information in a Wireless LAN (WLAN)-based indoor navigation system. We describe the benefit of using maNMp information in multiple algorithms of the system, including radio-map generation, tracking, semantic positioning and navigation. Then we discuss how to represent or model the indoor map to fulfill the requirements of intelligent algorithms. We believe that a vector-based multi-layer representation is the best choice for indoor location-aware system

Path planning for socially-aware humanoid robots

Designing efficient autonomous navigation systems for mobile robots involves consideration of the robotís environment while arriving at a systems architecture that trades off multiple constraints. We have architected a navigation framework for socially-aware autonomous robot navigation, using only the on-board computing resources. Our goal is to foster the development of several important service robotics applications using this platform. Our framework allows a robot to autonomously navigate in indoor environments while accounting for people (i.e., estimating the path of all individuals in the environment), respecting each individualís private space. In our design, we can leverage a wide number of sensors for navigation, including cameras, 2D and 3D scanners, and motion trackers. When designing our sensor system, we have considered that mobile robots have limited resources (i.e., power and computation) and that some sensors are costlier than others (e.g., cameras and 3D scanners stream data at high rates), requiring intensive computation to provide useful insight for real-time navigation. We tradeoff between accuracy, responsiveness, and power, and choose a Hokuyo UST-20LX 2D laser scanner for robot localization, obstacle detection and people tracking. We use an MPU-6050 for motion tracking. Our navigation framework features a low-power sensor system (< 5W) tailored for improved battery life in robotic applications while providing sufficient accuracy. We have completed a prototype for a Human Support Robot using the available onboard computing devices, requiring less than 60W to run. We estimate we can obtain similar performance, while reducing power by ~60%, utilizing low-power high-performance accelerator hardware and parallelized software.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Development of a robust real-time filtering algorithm for inertial sensor based navigation systems with zero velocity update

Currently many GPS (Global Positioning System) satellites orbit the Earth providing users with information on position anywhere in the world and in all weather conditions. Information is gathered from the orbiting satellites and is merged with information from base towers on earth to locate a person’s position. Although GPS is leading the navigation system industry it does suffer in that GPS signals are unable to pass through solid structures. This means GPS is unable to accurately work in dense urban areas or indoor environments. This research aims to develop a sensor based standalone indoor navigation system using a robust real-time filtering algorithm to accurately provide a person’s positions and movement. Despite the numerous research and development on indoor navigation systems, little work has been done on maximizing the accuracy of the indoor navigation systems for achieving pin-point localization. The system proposed within utilizes a foot mounted IMU (inertial measurement unit) comprised of several inertial sensors capable of tracking a wearer’s movements without satellite signals. IMU based systems, as with most other indoor navigation technologies, suffer from sensor “drift” during longtime navigation, which can cripple the system. This research aims to filter sensory data collected by an IMU system through an EKF (extended Kalman filter) to correct drift. An EKF is an optimal estimation algorithm capable of estimating dynamic variables of indirect and uncertain measurements. In the team’s endeavor to maximize the accuracy and efficiency of the algorithm they have found the integration of an EKF to be largely efficacious in mitigating drift error. As of right now, major drift error is still observed which overtime accumulates into false mapping, but solutions are in the process to mitigate this error. We feel that inertial based navigation systems, when paired with a real-time filtering algorithm, offer an alternative to GPS navigation far more conducive to indoor environment

Ego-Downward and Ambient Video based Person Location Association

Using an ego-centric camera to do localization and tracking is highly needed for urban navigation and indoor assistive system when GPS is not available or not accurate enough. The traditional hand-designed feature tracking and estimation approach would fail without visible features. Recently, there are several works exploring to use context features to do localization. However, all of these suffer severe accuracy loss if given no visual context information. To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach. Besides, we also proposed a novel action and motion verification model for cross-view verification and localization. We performed comparative experiments based on our collected dataset which considers the same dressing, gender, and background diversity. Results indicate that the proposed model can achieve $18.32 \%$ improvement in accuracy performance. Eventually, we tested the model on multi-people scenarios and obtained an average $67.767 \%$ accuracy

Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.Comment: 14 page