Unlike many current navigation approaches for micro air vehicles, the relative navigation (RN) framework presented in
this paper ensures that the filter state remains observable in GPS-denied environments by working with respect to a local
reference frame. By subtly restructuring the problem, RN ensures that the filter uncertainty remains bounded, consistent,
and normally-distributed, and insulates flight-critical estimation and control processes from large global updates. This paper
thoroughly outlines the RN framework and demonstrates its practicality with several long flight tests in unknown GPS-denied
and GPS-degraded environments. The relative front end is shown to produce low-drift estimates and smooth, stable control
while leveraging off-the-shelf algorithms. The system runs in real time with onboard processing, fuses a variety of vision
sensors, and works indoors and outdoors without requiring special tuning for particular sensors or environments. RN is shown
to produce globally-consistent, metric, and localized maps by incorporating loop closures and intermittent GPS measurements
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Relative multiplicative extended Kalman filter for observable GPS-denied navigation
This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle’s body frame, and provides insights into the subtle differences that arise between the two formulations.
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- Award ID(s):
- 1650547
- NSF-PAR ID:
- 10316379
- Date Published:
- Journal Name:
- The International Journal of Robotics Research
- Volume:
- 39
- Issue:
- 9
- ISSN:
- 0278-3649
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0278364920903094
