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Repetitive or ambiguous environment, where structures are highly similar and distinct geometric features are not sufficient, is one of the critical and challenging scenarios for mobile robots to perform global localization or simultaneous localization and mapping (SLAM) tasks. The robots are easy to get lost or mismatched to wrong places in such environments. The existing solutions either rely heavily on pre-installed infrastructures which are inflexible and expensive or rely on single sensor-based global localization methods whose initialization module is not capable enough to provide distinctive information. Thus, this article proposes a hierarchical probabilistic framework that addresses the problem of infrastructure-free mobile robot global localization in GPS-challenged repetitive environments by leveraging both the measured magnetic field (MF) and Light Detection and Ranging (LiDAR) information. The proposed hierarchical system mainly consists of: 1) coarse localization: MF-based localization and 2) fine localization: LiDAR-based localization (LBL). The ambient MF functions as a substitute for the GPS signal, which is considered not available or severely challenged in indoor/semi-indoor local environments. Based on the pre-built MF database, multiple coarse candidate robot poses can first be determined by the proposed initial pose estimation algorithm, which incorporates multivariate Gaussian observation model and random sample consensus (RANSAC) algorithm. Then, using the obtained multiple candidate poses as initialization, the robot can be localized more accurately by LiDAR-based fine localization. Extensive real-world experiments demonstrate that the proposed system achieves over 95% localization success rate and takes less than 2.0-m average traveling distance to localize the mobile robot. [ABSTRACT FROM AUTHOR]

Vehicle networks is a promising technique, which could provide ubiquitous wireless access for vehicles and realize intelligent transportation. Motivated by the recent advances on device-free wireless sensing, this paper explores a novel technique, which could estimate the speed of a vehicle by analyzing its influence on surrounding wireless signals from roadside wireless infrastructures, such as WiFi. This technique could enable vehicle networks with new moving speed sensing ability and evolve it into a large-scale sensing network, which could monitor the traffic status seamlessly. To achieve this goal, in this paper, we propose and formulate a model to characterize the relationship between the phase and amplitude measurements and the vehicle speed. Based on the model, we develop an efficient method to detect the vehicle and estimate its speed accordingly using the frequency-domain information involved in the spectrogram. We carry out extensive experiments utilizing commodity WiFi. The experimental results reveal that the proposed method could estimate the speed with an error of less than 2.6 km/h, which meets the United Nations regulation for speedometer. [ABSTRACT FROM AUTHOR]