The surface roughness of roads is an essential road characteristic. Due to the employed carrying platforms (which are often cars), existing measuring methods can only be used for motorable roads. Until now, there has been no effective method for measuring the surface roughness of un-motorable roads, such as pedestrian and bicycle lanes. This hinders many applications related to pedestrians, cyclists and wheelchair users. In recognizing these research gaps, this paper proposes a method for measuring the surface roughness of pedestrian and bicycle lanes based on Global Positioning System (GPS) and accelerometer sensors on bicycle-mounted smartphones. We focus on the International Roughness Index (IRI), as it is the most widely used index for measuring road surface roughness. Specifically, we analyzed a computing model of road surface roughness, derived its parameters with GPS and accelerometers on bicycle-mounted smartphones, and proposed an algorithm to recognize potholes/humps on roads. As a proof of concept, we implemented the proposed method in a mobile application. Three experiments were designed to evaluate the proposed method. The results of the experiments show that the IRI values measured by the proposed method were strongly and positively correlated with those measured by professional instruments. Meanwhile, the proposed algorithm was able to recognize the potholes/humps that the bicycle passed. The proposed method is useful for measuring the surface roughness of roads that are not accessible for professional instruments, such as pedestrian and cycle lanes. This work enables us to further study the feasibility of crowdsourcing road surface roughness with bicycle-mounted smartphones.

Background:The existing augmented reality (AR) based neuronavigation systems typically require markers and additional tracking devices for model registration, which causes excessive preparatory steps.Methods: For fast and accurate intraoperative navigation, this work proposes a marker-less AR system that tracks the head features with the monocular camera.After the semi-automatic initialization process, the feature points between the captured image and the pre-loaded keyframes are matched for obtaining correspondences. The camera pose is estimated by solving the Perspective-n-Point problem. Results:The localization error of AR visualization on scalp and falx meningioma is 0.417 � 0.057 and 1.413 � 0.282 mm, respectively. The maximum localization error is less than 2 mm. The AR system is robust to occlusions and changes in viewpoint and scale. Conclusions:We demonstrate that the developed system can successfully display the augmented falx meningioma with enough accuracy and provide guidance for neurosurgeons to locate the tumour in brain.

Navigation in a complex indoor environment can be difficult, and pedestrians may find themselves disoriented. As the featured objects of an environment, indoor landmarks play an important role in navigation. A review of the existing literature in outdoor landmark evaluation methods, however, shows that they cannot be fully applicable in any indoor environment. In this paper, an instance-based scoring system is proposed for analyzing the indicators that influence the salience of spatial objects from visual, semantic and structural aspects. An Analytic Hierarchy Process was applied to calculate landmark weights using these indicators. Two types of indoor scenes were employed as instances to verify the validity of this method, the Dongchenghui shopping mall (Nanjing, China) using a subjective questionnaire and interview method, and the headquarters of Masaryk University (Brno, Czechia) using an objective eyetracking method. Be result of the two instances showed that the proposed method was feasible.