Sparse Range Measurements Research Articles

2D Relative Pose and Scale Estimation with Monocular Cameras and Ranging

Cooperative swarms of robots equipped with cameras are robust against failures and can explore Global Navigation Satellite System-denied environments efficiently. Applying Visual Simultaneous Localization and Mapping (VSLAM) techniques, vehicles can estimate their trajectories and simultaneously reconstruct the map of the environment using visual cues. Due to constraints on payload size, weight, and costs, many Visual Simultaneous Localization and Mapping applications must be based on a single camera. The associated monocular estimation of the trajectory and map is ambiguous by a scale factor. This work shows that by exploiting sparse range measurements between a pair of dynamic rovers in planar motion, the correct scale factors of both cameras and the relative position, as well as the relative attitude between the rovers, can be estimated. Neither images nor feature vectors are required to be transmitted over the communication channel for the proposed method, which is a significant advantage in practice.

Visual mapping for natural gas pipe inspection

Validating the integrity of pipes is an important task for safe natural gas production and many other operations (e.g. refineries, sewers, etc.). Indeed, there is a growing industry of actuated, actively driven mobile robots that are used to inspect pipes. Many rely on a remote operator to inspect data from a fisheye camera to perform manual inspection and provide no localization or mapping capability. In this work, we introduce a visual odometry-based system using calibrated fisheye imagery and sparse structured lighting to produce high-resolution 3D textured surface models of the inner pipe wall. Our work extends state-of-the-art visual odometry and mapping for fisheye systems to incorporate weak geometric constraints based on prior knowledge of the pipe components into a sparse bundle adjustment framework. These constraints prove essential for obtaining high-accuracy solutions given the limited spatial resolution of the fisheye system and challenging raw imagery. We show that sub-millimeter resolution modeling is viable even in pipes which are 400 mm (16”) in diameter, and that sparse range measurements from a structured lighting solution can be used to avoid the inevitable monocular scale drift. Our results show that practical, high-accuracy pipe mapping from a single fisheye camera is within reach.

A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.

Resilient localization for sensor networks in outdoor environments

The process of determining the physical locations of nodes in a wireless sensor network is known as localization . Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire internode distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35 m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1% (33 cm). The localization algorithm is based on least-squares scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multidimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor network deployments.