Relative navigation of autonomous GPS-degraded micro air vehicles

Abstract

Many current approaches for navigation of micro air vehicles (MAVs) in GPS-degraded environments use a globally-referenced state for estimation and control, even though this state is not observable when GPS is unavailable. By working with respect to a local reference frame, the relative navigation (RN) framework presented in this paper ensures that the state maintains observability and that the uncertainty remains bounded, consistent, and normally-distributed. RN further insulates flight-critical estimation and control processes from the large global updates common in GPSdegraded MAV flight. This paper provides a thorough description of the details needed to successfully implement the RN framework on a MAV. The practicality of RN is demonstrated in 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, works indoors and outdoors, and does not require special tuning for particular sensors or environments. RN is also shown to produce globally-consistent, metric, and localized maps by incorporating loop closures and intermittent GPS measurements. This map is used to demonstrate autonomous completion of mission objectives. By subtly restructuring the estimation framework, RN promotes a paradigm shift that avoids many issues inherent in GPS-degraded navigation.

Relative Navigation: A Keyframe-Based Approach for Observable GPS-Degraded Navigation

“…This section is not intended to thoroughly describe all MAV implementation details, but rather to demonstrate that the performance of full-state estimators running on actual hardware parallels the results seen in simulation. Complete details on the relative navigation implementation used for these results, along with a presentation of more extensive flight-test results, are given in [21].…”

“…The following sections give highlevel descriptions of the relative front end and global back end. For further details on the RN approach and its implementation, the reader is referred to [20][21][22].…”

“…Because RN only estimates a relative state, directly fusing an eventual global measurement is not an option. Rather, these measurements are incorporated using a pose-graph map as described in [21]. This formulation completely avoids the problem of large jumps in the filter states that are used for control.…”

Relative multiplicative extended Kalman filter for observable GPS-denied navigation

“…Recent simulation results have shown that using the relative navigation framework to ensure observability provides significant advantages in terms of consistency of the estimated relative and global states, as well as some improvement in accuracy and system robustness when incorporating intermittent global information (Wheeler et al, 2018). Furthermore, recent multirotor hardware flight test results have demonstrated the effectiveness of relative navigation for prolonged GPS-degraded navigation of small UASs (Wheeler et al, 2020). This article compliments the work in Wheeler et al (2018) and Wheeler et al (2020) by providing a complete, self-contained, tutorial derivation of the relative state-estimation filter used in those papers to obtain hardware results.…”

Underwater confined space mapping by resource‐constrained autonomous vehicle

“…To drive the cost of platforms down, optical sensors in the form of monocular or stereo cameras have been used for obstacle detection and robot control using techniques such as optical flow (Engel, Sturm, & Cremers, ; Horn & Schunck, ; Souhila & Karim, ) or landmark feature extraction for long‐term odometric correction and localization (Goedeme et al., ; Guan et al., ; Klein & Murray, ). Aerial robotics has extended many of these algorithms into three dimensions, including closed‐loop visual navigation and visual‐SLAM (Achtelik, Achtelik, Weiss, & Siegwart, ; Achtelik et al., ; Blösch, Weiss, Scaramuzza, & Siegwart, ; Fraundorfer et al., ; Wheeler & Koch, ; Zingg, Scaramuzza, Weiss, & Siegwart, ) and general sensor fusion for state estimate (Garcia et al., ; López et al., ; Lynen et al., ); the capability to map enclosed buildings (Droeschel et al., ; Shen, Michael, & Kumar, ) has also been demonstrated.…”