@INPROCEEDINGS{leishman12relative,
  author = {Leishman, Robert and Macdonald, John and McLain, Timothy and Beard,
	Randal},
  title = {Relative Navigation and Control of a Hexacopter},
  booktitle = {Robotics and Automation (ICRA), 2012 IEEE International Conference
	on},
  year = {2012},
  month = {may},
  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.}
}

@INPROCEEDINGS{leishman11utilizing,
  author = {Leishman, Robert and Macdonald, John and Quebe, Stephen and Ferrin,
	Jeff and Beard, Randal and McLain, Timothy},
  title = {Utilizing an improved rotorcraft dynamic model in state estimation},
  booktitle = {Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International
	Conference on},
  year = {2011},
  pages = {5173 -5178},
  month = {sept.},
  abstract = {Multirotor aircraft have become a popular platform for indoor flight.
	To navigate these vehicles indoors through an unknown environment
	requires the use of a SLAM algorithm, which can be processing intensive.
	However, their size, weight, and power capacity limit the processing
	capabilities available onboard. In this paper, we describe an approach
	to state estimation that helps to alleviate this problem. By using
	an improved dynamic model we show how to more accurately estimate
	the aircraft states than can be done with the traditional approach
	of integrating IMU measurements. The estimation is done with relatively
	infrequent corrections from accelerometers (40Hz) and even less frequent
	updates from a vision-based SLAM algorithm (2 #x2013;5 Hz). This
	benefit of requiring less frequent updates from processing intensive
	sources comes without significant increase in the estimator's complexity.},
  doi = {10.1109/IROS.2011.6094922},
  issn = {2153-0858}
}

@INPROCEEDINGS{ferrin11differential,
  author = {Ferrin, Jeff and Leishman, Robert and Beard, Randy and McLain, Tim},
  title = {Differential flatness based control of a rotorcraft for aggressive
	maneuvers},
  booktitle = {Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International
	Conference on},
  year = {2011},
  pages = {2688 -2693},
  month = {sept.},
  abstract = {We propose a new method to control a multi-rotor aerial vehicle. We
	show that the system dynamics are differentially flat. We utilize
	the differential flatness of the system to provide a feed forward
	input. The system model derived allows for arbitrary changes in yaw
	and is not limited to small roll and pitch angles. We demonstrate
	in hardware the ability to follow a highly maneuverable path while
	tracking a time-varying heading command.},
  doi = {10.1109/IROS.2011.6095098},
  issn = {2153-0858}
}