Topic
Mobile robot navigation
About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.
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TL;DR: The case study elaborates the proposed localization methods (viz., topological, landmark, metric, crowdsourced, and sound localization) for applications in way finding, way confirmation, user tracking, socialization, and situation alerts.
Abstract: This paper provides a framework for context-aware navigation services for vision impaired people. Integrating advanced intelligence into navigation requires knowledge of the semantic properties of the objects around the user's environment. This interaction is required to enhance communication about objects and places to improve travel decisions. Our intelligent system is a human-in-the-loop cyber-physical system that interprets ubiquitous semantic entities by interacting with the physical world and the cyber domain, viz., 1) visual cues and distance sensing of material objects as line-of-sight interaction to interpret location-context information, and 2) data (tweets) from social media as event-based interaction to interpret situational vibes. The case study elaborates our proposed localization methods (viz., topological, landmark, metric, crowdsourced, and sound localization) for applications in way finding, way confirmation, user tracking, socialization, and situation alerts. Our pilot evaluation provides a proof of concept for an assistive navigation system.
72 citations
Patent•
02 May 2000TL;DR: In this paper, an integrated navigation system includes a prime mission navigation system (e.g., a military code GPS-based flight management system), a second navigation system, and an integrity check system.
Abstract: An integrated navigation system includes a prime mission navigation system (e.g., a military code GPS-based flight management system), a second navigation system (e.g., a civil code GPS-based navigation system) and an integrity check system. The first navigation system receives first positioning signals and generates a first navigation solution based on the first positioning signals. The second navigation system receives second positioning signals and generates a second navigation solution based on the second positioning signals. The integrity check system receives the first navigation solution and the second navigation solution, compares the first navigation solution to the second navigation solution, and generates a validity signal based on the comparison.
72 citations
TL;DR: Two sensor-fusion steps implemented in commercial Siemens car navigation systems are described, the first is the fusion of the odometer, gyroscope, and GPS sensory information, and the second is the use of the available digital map to find the most likely position on the roads.
Abstract: Car navigation systems have three main tasks, namely 1) positioning; 2) routing; and 3) navigation (guidance). Positioning of the car is carried out by appropriately combining information from several sensors and information sources, including odometers, gyroscopes, Global Positioning System (GPS) information, and digital maps. This paper describes two sensor-fusion steps implemented in commercial Siemens car navigation systems. The first step is the fusion of the odometer, gyroscope, and GPS sensory information. The dynamic model of the car movement is implemented in a Kalman filter, which relays the GPS signal as a teacher. In the second step, the available digital map is used to find the most likely position on the roads. Contrary to the standard application of the digital map, where the current estimated car position is just projected on the road map, the approach presented here compares the features of the integrated vehicle path with the features of the candidate roads from the digital map. In addition, this paper presents the results of the experimental drives. The developed car navigation system was awarded the best car navigation system among ten competing systems in 2002 by the Auto Build magazine
72 citations
TL;DR: A qualitative, topological and robust world-modelling technique with special regard to navigation-tasks for mobile robots operating in unknownenvironments, with very low constraints for the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform.
72 citations
01 Sep 2013
TL;DR: A robot navigation algorithm, called social-aware navigation, which is mainly driven by the social-forces centered at the robot, is proposed, which uses a MCMC Metropolis-Hastings algorithm in order to learn the parameters values of the method.
Abstract: In this paper we present a novel robot navigation approach based on the so-called Social Force Model (SFM). First, we construct a graph map with a set of destinations that completely describe the navigation environment. Second, we propose a robot navigation algorithm, called social-aware navigation, which is mainly driven by the social-forces centered at the robot. Third, we use a MCMC Metropolis-Hastings algorithm in order to learn the parameters values of the method. Finally, the validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.
72 citations