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Two suitable coordinate systems overlapping at the origin were established to estimate orientation by computing the spatial relationship between them. An Inertial Measurement Unit sensor (IMU), consisting of a tri-axial gyroscope and tri-axial accelerometer was used to define reference systems. This work describes an algorithm to estimate the orientation of an object in 3D space through the optimal...
Urban environment constitutes a challenging area for pedestrian navigation. However, with the recent increase of pedestrians owning devices (e.g. smartphones), complementary data provided by integrated low cost sensors (camera, Inertial and Magnetic Measurement Unit and GNSS receiver) may be used in a coupling process to accurately estimate the pose (i.e. 3D position and 3D orientation) of a handheld...
Considered as the free accessible and suitable solution for positioning in urban areas, Global Navigation Satellite Systems (GNSS) have been widely used these recent years in a wide spectrum of applications. However, signal blockage, non-line-of-sight (NLOS) multipath interferences and signal degradation affect the system performance and represent the major hurdles of GNSS in it course of adoption...
Many current LIDAR systems generate huge amounts of 3D points, therefore efficient and fast data processing is essential. In urban areas surfaces are often planar and plane patches are an efficient representation for localization purposes. Our approach thus stores the points in a voxel grid and afterwards extracts the dominant plane using RANSACbased plane fitting. Classical RANSAC-based plane extraction...
More and more pedestrians own devices (as a smartphone) that integrate a wide array of low-cost sensors (camera, IMU, magnetometer and GNSS receiver). GNSS is usually used for pedestrian localization in urban environment, but signal suffers of an inaccuracy of several meters. In order to have a more accurate localization and improve pedestrian navigation and urban mobility, we present a method for...
In this paper, we aim at detecting vehicles from the point clouds scanned from the urban area. Our detection method consists of a segmentation stage and a classification stage. Prior knowledge for vehicles and urban environment is utilized to help the detection process. Specifically, we incorporate curb detection and removal in the segmentation stage. Moreover, our approach is able to estimate the...
Multi-scale collaborative virtual environments (MCVEs) provide an important platform for many 3D application domains as they allow several users to cooperate in a virtual environment (VE) at different scale levels, ranging from magnified detail views to minified overall views. However, in such MCVEs, the natural relations between a user's self-representation, i. e., her virtual body, and the environment...
The building of an object-level knowledge base is the foundation of a new methodology for many perception tasks in artificial intelligence, and is an area that has received increasing attention in recent years. In this paper, we propose, for the first time, to mine category shape patterns directly from a large urban environment, thus constructing a category structure base. Conventionally, category...
This paper presents a precise localization algorithm for vehicles in 3D urban environment with only one 2D LIDAR and odometry information. A novel idea of synthetic 2D LIDAR is proposed to solve the localization problem on a virtual 2D plane. A Monte Carlo Localization scheme is adopted for vehicle position estimation, based on synthetic LIDAR measurements and odometry information. The accuracy and...
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