Collaborative navigation with ground vehicles and personal navigators

Abstract

An integrated positioning solution termed 'collaborative positioning' employs multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UA V's, team of robots, emergency crews and first responders. The stakeholders of the solution (i.e., mobile sensors, users, fixed stations and external 978-1-4673-1954-6/12/$31.00 databases) are involved in an iterative algorithm to estimate or improve the accuracy of each node's position based on statistical models. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation, and partially indoor navigation. For this purpose different sensor 2012 International Conference on Indoor Positioning and Indoor Navigation, 13_15 th November 2012 platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans, a personal navigator and a foot tracker unit. In terms of the tests, the data from the different platforms are recorded simultaneously. Several field experiments conducted in a week at the University of Nottingham are described and investigated in the paper. The personal navigator and a foot tracker unit moved on the building roof, then trough the building down to where it logged data simultaneously with the vans, all of them moving together and relative to each other. The platforms then logged data simultaneously covering various accelerations, dynamics, etc. over longer trajectories. Promising preliminary results of the field experiments showed that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms.

Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?

“…In this approach, the results are not only more robust when compared to independent solutions computed by individual nodes but more significantly, they afford enhanced positioning capabilities in difficult environments where for example, in the case of GNSS, there is multipath or complete obscuration of satellite signals. It has been demonstrated [36,37,38,39,40] that collaborative navigation not only enables navigation in environments where a single user cannot navigate, but also increases the accuracy of the navigation solution by several orders of magnitude. Similar to a loosely coupled architecture, the data shared can be simply positions and their variances as determined by the onboard sensors or, in a more tightly coupled architecture, the measurements made by each node can be integrated directly.…”

Collaborative Navigation as a Solution for PNT Applications in GNSS Challenged Environments – Report on Field Trials of a Joint FIG / IAG Working Group

“…Collectively, a network of GNSS users (hereafter referred to as nodes) may be able to receive sufficient satellite signals, augmented by inter-nodal ranging measurements and other sensors, such as IMUs or active / passive imaging sensors, in order to form a joint position solution [15,17,18,22,25,26,46]. This network of GNSS users represents a distributed antenna aperture with large inter-element spacing, which has some advantages and also drawbacks.…”

“…near the university campus to test the DSRC performance. Figure 11 shows the two mobile mapping vans equipped with the sensors listed in Table 5 and 6. First results of these experiments are presented in the papers [22][23][24]. They indicate that CP is capable of providing significant navigation improvements, as well as enabling navigation in otherwise challenging environments.…”

Image Navigation Using A Tracking-Based Approach

“…Many incorporate inexpensive INS [2], [8]. It is well known, though, that these inertial systems have drifts and biases that without corrections will grow [8], [9]. For short periods of time and for operations where an human can control the motion of the navigating object easily, these systems are useful.…”