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Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.

The objective of this work is to estimate the localization and attitude of a land-vehicle by fusing GPS, OSM and DEM data through a nonlinear filter. We focus on the heading and pitch angles of the vehicle, knowing that these parameters are essential in the optimization of the route planning and energy management for an EV. This paper investigates the performance of particle filtering and probabilistic map-matching algorithms for tracking a vehicle with the help of digital roadmaps to improve the ground-location. Also, the filter fuses DEM data through a TIN method in order to bound altitude errors caused by GPS. The proposed method is evaluated through an urban transport network scenario and experimental results show that the proposed estimator can accurately estimate the vehicle location and attitude.

As people age, they become more fragile and exhibit difficulties in maintaining their gait and balance. Their state of fragility increases their vulnerability to fall incidents. Various analysis methods were developed to detect the abnormality of human gait and balance, and estimate the risk of falling. In this paper, we present a method to estimate the falling risk and alert the patient when a fall is about to happen. The proposed method consists in monitoring and analyzing the amount of sway of the center of mass in the medial-lateral plane by computing the center of pressure displacement at the foot plantar surface. Our proposed method uses the spatio-temporal sliding window processing to generate fall alarms and estimate the falling risk. The method was validated via a two-phase experimental protocol with five young adults who performed a walk of 20 stances with simulated sways using an instrumented shoe with resistive pressure sensors. The threshold of the normal walk TH N and the risk level R L of the altered walk are determined as well as the risk of falling. The method can be applied in real-life and clinical settings with real-time processing.