Localization is a critical capability for robots, drones and autonomous vehicles operating in a wide range of environments. One of the critical considerations for designing, training or calibrating visual localization systems is the coverage of the visual sensors equipped on the platforms. In an aerial context for example, the altitude of the platform and camera field of view plays a critical role in how much of the environment a downward facing camera can perceive at any one time. Furthermore, in other applications, such as on roads or in indoor environments, additional factors such as camera resolution and sensor placement altitude can also affect this coverage. The sensor coverage and the subsequent processing of its data also has significant computational implications. In this paper we present for the first time a set of methods for automatically determining the trade-off between coverage and visual localization performance, enabling the identification of the minimum visual sensor coverage required to obtain optimal localization performance with minimal compute. We develop a localization performance indicator based on the overlapping coefficient, and demonstrate its predictive power for localization performance with a certain sensor coverage. We evaluate our method on several challenging real-world datasets from aerial and ground-based domains, and demonstrate that our method is able to automatically optimize for coverage using a small amount of calibration data. We hope these results will assist in the design of localization systems for future autonomous robot, vehicle and flying systems.
In several mobile robotics applications the system moves relative to a surface, such as a drone across the ground, an autonomous vehicle over the road or a submarine relative to a ship's hull. As a result, several approaches have proposed using the surface that the robot moves relative to as a visual reference map for localization. For example, thoroughly demonstrated that surface-based visual localization using pixel-based techniques for mobile ground platforms is feasible within warehouse environments with controlled lighting using a monocular camera @cite_31 @cite_7 . also demonstrated this technique can be applied to autonomous vehicles and a road surface, even with day to night image data @cite_40 . red Additionally, @cite_13 @cite_37 demonstrate the use of local features for road surface-based visual localization.
@cite_37 @cite_7 @cite_40 @cite_31 @cite_13
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Location-aware applications play an increasingly critical role in everyday life. However, the most common global localization technology - GPS - has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local features in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of carpet fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces. Automated guided vehicles (AGVs) have been operating effectively in factories for decades. These vehicles have successfully used strategies of deliberately structuring the environment and adapting the process to the automation. The potential of computer vision technology to increase the intelligence and adaptability of AGVs is largely unexploited in contemporary commercially available vehicles. We developed an infrastructure-free AGV that uses four distinct vision systems. Three of them exploit naturally occurring visual cues instead of relying on infrastructure. When coupled with a highly capable trajectory generation algorithm, the system produces four visual servo controllers that guide the vehicle continuously in several contexts. These contexts range from gross motion in the facility to precision operations for lifting and mating parts racks and removing them from semi-trailers. To our knowledge, this is the first instance of an AGV that has operated successfully in a relevant environment for an extended period of time without relying on any infrastructure. A new practical, high-performance mobile robot localization technique is described that is motivated by the fact that many man-made environments contain substantially flat, visually textured surfaces of persistent appearance. While the tracking of image regions is much studied in computer vision, appearance is still a largely unexploited localization resource in commercially relevant applications. We show how prior appearance models can be used to enable highly repeatable mobile robot guidance that, unlike commercial alternatives, is both infrastructure-free and free-ranging. Very large-scale mosaics are constructed and used to localize a mobile robot operating in the modeled environment. Straightforward techniques from vision-based localization and mosaicking are used to produce a field-relevant AGV guidance system based only on vision and odometry. The feasibility, design, implementation, and precommercial field qualification of such a guidance system are described. This paper presents an overview of the Ranger localization system and its constituent technologies and algorithms. Ranger is a high-precision localization system for ground vehicles that performs map-based localization using a ground-facing camera. Ranger uses commercially available hardware, including a camera, lights, and a computer, in combination with auxiliary localization sensors and a custom state estimator, to produce a complete high-precision positioning solution suitable for feedback control of an automated vehicle. Ranger was originally conceived and designed to address the accuracy and availability problems of GPS, and can operate independent from or as a supplement to GPS and other GNSS positioning systems. Ranger position measurements are made by matching live ground imagery to imagery stored in a map. The image matching process uses a feature-based approach that yields a high positive match rate with a vanishingly small false positive rate. The precision of the Ranger measurements is shown to be on the order of centimeters, and the actual lateral positioning performance of MARTI, one of Southwest Research Institute's automated vehicles, autonomously driving a test route is shown to be repeatable over many runs to within 2 cm.