Relative navigation and control of a hexacopter

Abstract

This paper discusses the progress made on developing a multi-rotor helicopter equipped with a vision-based ability to navigate through an a priori unknown, GPS-denied environment. We highlight the backbone of our system, the relative estimation and control. We depart from the common practice of using a globally referenced map, preferring instead to keep the position and yaw states in the EKF relative to the current map node. This relative navigation approach allows simple application of sensor updates, natural characterization of the transformation between map nodes, and the potential to generate a globally consistent map when desired. The EKF fuses view matching data from a Microsoft Kinect with more frequent IMU data to provide state estimates at rates high enough to control the vehicles fast dynamics. Although an EKF is used, a nodes and edges graph represents the map. Hardware results showing the quality of the estimates and flights with estimates in the loop are provided.

Multiplicative Extended Kalman Filter for Relative Rotorcraft Navigation

“…The main contribution of this article is the development of a multiplicative extended Kalman filter (MEKF) that uses an improved rotorcraft model [14] to provide relative, rather than global, state estimates. Another contribution is a more detailed development of the relative navigation approach originally described in [19] and the relative filtering approach described in [18]. Additionally, we verify the results of the relative estimation approach with hardware flight tests accompanied by comparisons to motion capture truth data.…”

“…In [18], [19], the authors propose that a vehicle should navigate using a relative formulation of the vehicle state, rather than a global one. Similar to the general approach, they use a combination of graph SLAM and an EKF to provide mapping and sensor fusion.…”

Non-Redundant Sensor Fault Detection for Autonomous Rotorcraft using an Improved Dynamic Model

“…In [20] and [21], the authors propose that a vehicle should navigate using a relative formulation of the vehicle state, rather than a global one. A combination of graph SLAM and an EKF is used to provide mapping and sensor fusion.…”

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

“…The resulting scheme utilizes IMU state and covariance propagation information to aid the feature matching of the stereo vision, leading to increased efficiency and robustness of MAV state estimation in complex industrial environments. In addition, EKF based methods have also proven useful for RGB-D vision-aided navigation of MAVs, and relevant examples can be found in [17,18]. Among the variants of the EKFs, multiplicative extended Kalman filters (MEKF) are especially useful for MAV attitude estimation applications.…”

“…RGB-D devices are based on structured light technologies and can provide depth data even in poorly textured environments. Taking advantage of RGB-D devices, many researchers have achieved successful results in the field of indoor MAV navigation, such as state estimation, control and indoor mapping [17,18]. Despite these advances achieved in this domain, there is still significant progress to be made in developing more robust and computationally efficient visual odometry approaches for MAVs in complex environments, using lightweight and low-cost RGB-D devices and MEMS sensors.…”