Drones hold the promise of massive positive impact Existing use cases are easier and more reliable New use cases that were previously impossible are enabled

We present CELLO (Covariance Estimation and Learning through Likelihood Optimization), an algorithm for predicting the covariances of measurements based on any available informative features. This algorithm is intended to improve the accuracy and reliability of on-line state estimation by providing a principled way to extend the conventional fixed-covariance Gaussian measurement model. We show that in experiments, CELLO learns to predict measurement covariances that agree with empirical covariances obtained by manually annotating sensor regimes. We also show that using the learned covariances during filtering provides substantial quantitative improvement to the overall state estimate.

In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment.

This video highlights our system that enables a Micro Aerial Vehicle (MAV) to autonomously explore and map unstructured and unknown GPS-denied environments. While mapping and exploration solutions are now well-established for ground vehicles, air vehicles face unique challenges which have hindered the development of similar capabilities. Although there has been recent progress toward sensing, control, and navigation techniques for GPS-denied flight, there have been few demonstrations of stable, goal-directed flight in real-world environments. Our system leverages a multi-level sensing and control hierarchy that matches the computational complexity of the component algorithms with the real-time needs of a MAV to achieve autonomy in unconstrained environments.

A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target's pose, and must reason about its uncertainty of the targets' poses when planning subsequent actions. We present an online, forward-search algorithm for planning under uncertainty by representing the agent's belief of each target's pose as a multi-modal Gaussian belief. We exploit this parametric belief representation to directly compute the distribution of posterior beliefs after actions are taken. This analytic computation not only enables us to plan in problems with continuous observation spaces, but also allows the agent to search deeper by considering policies composed of multi-step action sequences; deeper searches better enable the agent to keep the targets well-localized. We present experimental results in simulation, as well as demonstrate the algorithm on an actual quadrotor helicopter tracking multiple vehicles on a road network constructed indoors.

This paper describes a new algorithm for cooperative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph representations have proven very successful for single robot mapping and localization. Among these methods, incremental smoothing and mapping (iSAM) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization problem in real-time. In this paper, we present a novel extension to iSAM to facilitate online multi-robot mapping based on multiple pose graphs. Our main contribution is a relative formulation of the relationship between multiple pose graphs that avoids the initialization problem and leads to an efficient solution when compared to a completely global formulation. The relative pose graphs are optimized together to provide a globally consistent multi-robot solution. Efficient access to covariances at any time for relative parameters is provided through iSAM, facilitating data association and loop closing. The performance of the technique is illustrated on various data sets including a publicly available multi-robot data set. Further evaluation is performed in a collaborative helicopter and ground robot experiment.

Natural language is a flexible and intuitive modality for conveying directions and commands to a robot but presents a number of computational challenges. Diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. In this paper we present a micro-air vehicle (MAV) capable of following natural language directions through a previously mapped and labeled environment. We extend our previous work in understanding 2D natural language directions to three dimensions, accommodating new verb modifiers such as go up and go down, and commands such as turn around and face the windows. We demonstrate the robot following directions created by a human for another human, and interactively executing commands in the context of surveillance and search and rescue in confined spaces. In an informal study, 71% of the paths computed from directions given by one user terminated within 10 m of the desired destination.