Relative Navigation with Displacement Measurements and its Absolute Correction

C. Yang, A. Soloviev

Abstract:	Plug and play (PnP) navigation is based on measurement abstraction such that the integration filter can handle a variety of sensors of different nature. The navigation mechanization is formulated as an object oriented design where different sensor categories are represented by generic classes in the software library. When a new sensor is connected to the system, the PnP architecture is automatically reconfigured by instantiating a corresponding sensor object, without any development and coding anew. One generic category of sensor category is linear and angular displacements, which accounts for a large number of diverse sensors. The most famous one is the inertial measurement unit (IMU) made of accelerometers and gyros. Other examples include imaging sensors (feature tracking and optical flow), wheeled odometers, step sensors (pedometers), barometers (altimeters), magnetic compass, and radio Doppler. When integrated over time from an initial condition, the displacement measurements provide a relative navigation solution, often referred to as a dead-reckoning. A definite advantage of dead-reckoning is its self-contained, passive operation, that is not susceptible jamming and interference. However, a major problem with dead-reckoning solutions is the accumulation of sensor bias and drift, which leads to unbounded errors, thus limiting the practical values of unaided dead-reckoning mechanizations. In cases where no external navigation aids (navaids) are available, displacement sensors of different types (e.g., inertial and camera) can be used to perform mutual calibration and aiding. If implemented properly, the resulting trajectory maintains the correct shape and size yet may be shifted and mis-oriented to a certain degree. Such a solution is normally still sufficient for missions that reply on relative navigation. Then, the use of any absolute measurements can snap navigation outputs back to the global reference and correct the error build-up. In this paper, we will present our PnP navigation algorithm using mutual calibration of linear and angular displacement sensors (cameras and IMUs) and absolute correction with non-precision measurements (RFID tags and geomagnetic fields). The algorithm was tested on all nineteen scenarios of DARPA ASPN Phase 1 datasets collected in urban and indoor environments. Three test results for Scenarios 1, 11, and 12, respectively, will be used to demonstrate the functionality and performance of our PnP navigation mechanization.
Published in:	Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013) September 16 - 20, 2013 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	2084 - 2093
Cite this article:	Yang, C., Soloviev, A., "Relative Navigation with Displacement Measurements and its Absolute Correction," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 2084-2093.
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