Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan

A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision

Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited.

FloorVLoc: A Modular Approach to Floorplan Monocular Localization

Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.

Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System

Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called “X-in-the-Loop (XIL)”. However, the validation of advanced driver assistance system (ADAS) controllers is more complicated and needs more environmental resources because the controller interacts with the external environment of the vehicle. Vehicle-in-the-Loop (VIL) is a recently being developed approach for simulating ADAS vehicles that ensures the safety of critical test scenarios in real-world testing using virtual environments. This new test method needs both properties of traditional computer simulations and real-world vehicle tests. This paper presents a Vehicle-in-the-Loop topology for execution in global Coordinates system. Also, it has a modular structure with four parts: synchronization module, virtual environment, sensor emulator and visualizer, so each part can be developed and modified separately in combination with other parts. This structure of VIL is expected to save maintenance time and cost. This paper shows its acceptability by testing ADAS on both a real and the VIL system.