We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile ARM processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a local depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV.

Avian flight far exceeds our best aircraft control systems. We have conducted a series of experiments at the Concord Field Station demonstrating the extraordinary maneuverability of the common pigeon, showing it darting through tight spaces and recovering from large disturbances with ease. Our goal is to understand how to make small fixed-wing aircraft achieve similar feats in equally challenging environments.

Unmanned aerial vehicles (UAVs) have a so-far untapped potential to operate at high speeds through cluttered environments. Many of these systems are limited by their adhoc reactive controllers using simple visual cues like optical flow. Here we consider the problem of formally verifying an output-feedback controller for an aircraft operating in an unknown environment. Using recent advances in sums-of-squares programming that allow for efficient computation of barrier functions, we search for global certificates of safety for the closed-loop system in a given environment. In contrast to previous work, we use rational functions to globally approximate non-smooth dynamics and use multiple barrier functions to guard against more than one obstacle. We expect that these formal verification techniques will allow for the comparison, and ultimately optimization, of reactive controllers for robustness to varying initial conditions and environments.

This paper presents our work in solving one of the weakest links in 802.11-based indoor-localization: the training of ground-truth received signal strength data. While crowdsourcing this information has been demonstrated to be a viable alternative to the time consuming and accuracy-limited process of manual training, one of the chief drawbacks is the rate at which a system can be trained. We demonstrate an approach that utilizes users' calendar and appointment information to perform interactionless training of an 802.11-based indoor localization system. Our system automatically determines if a user attended a calendar event, resulting in accuracy comparable to our previously published large-scale crowdsourced deployment. We find that no other user interaction is necessary to train the system to that level of accuracy when calendar data are available. In ideal conditions, this technique can reduce training time by over a factor of six.

Automated estimation of DNA base-sequences is an important step in genomics and in many other emerging fields in biological and medical sciences. Current automated sequencers process single strands only. To improve the utility of existing technologies, we propose to mix two independent strands prior to electrophoresis, and base-call jointly by applying the sum-product algorithm on factor graphs. We first present a statistical model for DNA sequencing data and examine the model parameters. A practical heuristic is then proposed to estimate the peaks, which are then separated into two source sequences (Major/Minor) by passing messages on a factor graph. Simulation results show that joint base-calling can provide less accurate but valid results for the minor. The algorithm presented provides a basis for future investigation of joint sequencing techniques.