In this paper we introduce the core results of the project on visualisation and analysis of data collected from the vertical transport facilities. The aim of the project was to provide better user experience as well as to help building maintenance staff to increase productivity of their work. We elaborated a web-based system for vertical transportation, to cover the needs of (1) staff working on building maintenance, (2) people who are regularly using the facilities in the corresponding buildings.
The results of a project on automated maintenance inspection, performance monitoring and ride quality measurement in vertical transportation systems were presented in @cite_5 . This approach is the most related to the work we present in this paper: The approach enabled lift performance profiles to be compiled automatically and transmitted in real time. The authors demonstrated that this solution significantly rationalized and improved the process of maintenance inspection and monitoring.
@cite_5
2411390475 1577920937 1573725801 2056358962
In this project, mobile connectivity and an innovative approach to sensor data gathering and integration have been employed to automate maintenance inspection, performance monitoring and ride quality measurement in vertical transportation systems. An Inertial Navigation System (INS) has been proposed, implemented and tested to track lift car movement profile. The inherent characteristics of vertical motion have been used to minimize errors and obtain higher accuracy in the integration results. The measurement of a correlation between kinematic profiles constructed from lift-car tracking data compared to its nominal values provides key information on the lift condition at any time. A frequency analysis was applied to processing vibrations and noise data, effectively adding another dimension to the lift ride quality measurement. This approach enabled lift performance profiles to be compiled automatically and transmitted in real time, which significantly rationalized and improved the process of maintenance inspection and monitoring. An advanced prototype, AdInspect, has been produced, with the full set of described features. Industry partners are currently evaluating it. Advances in autonomous inspection of subsea facilities using a 3 Dimensional(3D) LIght Detection And Ranging (LiDAR) sensor are examined to illustrate the favorable enhancement of safety, reliability, reduction in risks, economic benefits and superior data products compared to conventional means. These benefits provide operators with significant improvements over general visual inspection by the addition of sensors that produce 3D models of the structure being inspected. Examples are provided illustrating test data from operations conducted from 2012-2013. Supported by funding from the Research Partnership to Secure Energy for America (RPSEA) Lockheed Martin MST teamed with the innovative company 3D at Depth to incorporate their new DP2TM 3D LiDAR onto the proven Marlin® Autonomous Underwater Vehicle. The objectives of the project include: Survey using combination of high resolution 3D Sonar and 3D LiDAR Generation of high fidelity 3D models Detection and localization of structural changes vs. reference model Lockheed Martin has successfully integrated the laser into the Marlin shore based laboratory addressing the challenges associated with coordinating a scanning laser from a moving platform and producing high resolution georegistered images. Testing will be conducted in early 2014 to validate the system in the Atlantic Ocean waters offshore Palm Beach, Florida. Three dimensional georegistered models of an entire scene can be rapidly collected providing a clear vision of the underwater scene along high resolution 3D models of the imaged item. Data collected at sea and in the laboratory will be presented demonstrating the performance of the system. Application to deepwater life of field inspection will be presented with evidence gained from offshore trials. This emergent technology supports Subsea Facility Inspection Repair and Maintenance, Integrity Management Inspections of Marine Risers, Moorings and anchors, Subsea Pipelines, Flowlines, Umbilicals, and supporting subsea infrastructure. We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation. In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors. We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of extended Kalman filter (EKF)-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the multi-state-constraint Kalman filter (MSCKF)) attains better accuracy, consistency, and computational efficiency than the simultaneous localization and mapping (SLAM) formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF's linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-inertial measurement unit (IMU) calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte Carlo simulations and real-world testing.