Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.

/pdf/learning-monocular-reactive-uav-control-in-cluttered-natural-50kn7zimb0.pdf

Learning monocular reactive UAV control in cluttered natural environments

Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement comes at the cost of increased computational complexity. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes. The preintegration allows us to accurately summarize hundreds of inertial measurements into a single relative motion constraint. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation. The measurements are integrated in a local frame, which eliminates the need to repeat the integration when the linearization point changes while leaving the opportunity for belated bias corrections. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated in a visual-inertial pipeline under the unifying framework of factor graphs. This enables the use of a structureless model for visual measurements, further accelerating the computation. The third contribution is an extensive evaluation of our monocular VIN pipeline: experimental results confirm that our system is very fast and demonstrates superior accuracy with respect to competitive state-of-the-art filtering and optimization algorithms, including off-the-shelf systems such as Google Tango

http://www.roboticsproceedings.org/rss11/p06.pdf

IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large appearance change due to factors such as scale, motion, shape deformation, and occlusion. One of the main reasons is the lack of effective image representation schemes to account for appearance variation. Most of the trackers use high-level appearance structure or low-level cues for representing and matching target objects. In this paper, we propose a tracking method from the perspective of midlevel vision with structural information captured in superpixels. We present a discriminative appearance model based on superpixels, thereby facilitating a tracker to distinguish the target and the background with midlevel cues. The tracking task is then formulated by computing a target-background confidence map, and obtaining the best candidate by maximum a posterior estimate. Experimental results demonstrate that our tracker is able to handle heavy occlusion and recover from drifts. In conjunction with online update, the proposed algorithm is shown to perform favorably against existing methods for object tracking. Furthermore, the proposed algorithm facilitates foreground and background segmentation during tracking.

/pdf/robust-superpixel-tracking-3wptoo16le.pdf

Robust Superpixel Tracking

The boom in the capabilities and features of mobile devices, like smartphones, tablets, and wearables, combined with the ubiquitous and affordable Internet access and the advances in the areas of cooperative networking, computer vision, and mobile cloud computing transformed mobile augmented reality (MAR) from science fiction to a reality. Although mobile devices are more constrained computationalwise from traditional computers, they have a multitude of sensors that can be used to the development of more sophisticated MAR applications and can be assisted from remote servers for the execution of their intensive parts. In this paper, after introducing the reader to the basics of MAR, we present a categorization of the application fields together with some representative examples. Next, we introduce the reader to the user interface and experience in MAR applications and continue with the core system components of the MAR systems. After that, we discuss advances in tracking and registration, since their functionality is crucial to any MAR application and the network connectivity of the devices that run MAR applications together with its importance to the performance of the application. We continue with the importance of data management in MAR systems and the systems performance and sustainability, and before we conclude this survey, we present existing challenging problems.

Mobile Augmented Reality Survey: From Where We Are to Where We Go

We present an efficient algorithm for continuous image recognition and feature descriptor tracking in video which operates by reducing the search space of possible interest points inside of the scale space image pyramid. Instead of performing tracking in 2D images, we search and match candidate features in local neighborhoods inside the 3D image pyramid without computing their feature descriptors. The candidates are further validated by fitting to a motion model. The resulting tracked interest points are more repeatable and resilient to noise, and descriptor computation becomes much more efficient because only those areas of the image pyramid that contain features are searched. We demonstrate our method on real-time object recognition and label augmentation running on a mobile device.

http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR_2009/data/papers/0153.pdf

SURFTrac: Efficient tracking and continuous object recognition using local feature descriptors

©Copyright 2012 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.919598

https://www.cc.gatech.edu/~dellaert/pubs/Roberts12spie.pdf

Saliency detection and model-based tracking: a two part vision system for small robot navigation in forested environment

In this paper, we present a factor graph framework to solve both estimation and deterministic optimal control problems, and apply it to an obstacle avoidance task on Unmanned Aerial Vehicles (UAVs). We show that factor graphs allow us to consistently use the same optimization method, system dynamics, uncertainty models and other internal and external parameters, which potentially improves the UAV performance as a whole. To this end, we extended the modeling capabilities of factor graphs to represent nonlinear dynamics using constraint factors. For inference, we reformulate Sequential Quadratic Programming as an optimization algorithm on a factor graph with nonlinear constraints. We demonstrate our framework on a simulated quadrotor in an obstacle avoidance application.

/pdf/a-factor-graph-approach-to-estimation-and-model-predictive-357e82bo47.pdf

A Factor Graph Approach to Estimation and Model Predictive Control on Unmanned Aerial Vehicles

This paper studies an optimization-based approach for solving optimal estimation and optimal control problems through a unified computational formulation. The goal is to perform trajectory estimation over extended past horizons and model-predictive control over future horizons by enforcing the same dynamics, control, and sensing constraints in both problems, and thus solving both problems with identical computational tools. Through such systematic estimation-control formulation we aim to improve the performance of autonomous systems such as agile robotic vehicles. This work focuses on sequential sweep trajectory optimization methods, and more specifically extends the method known as differential dynamic programming to the parameter-dependent setting in order to enable the solutions to general estimation and control problems.

https://www.cc.gatech.edu/~dellaert/pub/Kobilarov15icra.pdf

Differential dynamic programming for optimal estimation

We propose a solution toward the problem of autonomous flight and exploration in man-made indoor environments with a micro aerial vehicle (MAV), using a frontal camera, a downward-facing sonar, and an IMU. We present a general method to detect and steer an MAV toward distant features that we call vistas while building a map of the environment to detect unexplored regions. Our method enables autonomous exploration capabilities while working reliably in textureless indoor environments that are challenging for traditional monocular SLAM approaches. We overcome the difficulties faced by traditional approaches with Wall-Floor Intersection Features, a novel type of low-dimensional landmarks that are specifically designed for man-made environments to capture the geometric structure of the scene. We demonstrate our results on a small, commercially available quadrotor platform.

https://www.cc.gatech.edu/~dellaert/pub/Ok12vicomor.pdf

Duy-Nguyen Ta

Papers

SURFTrac: Efficient tracking and continuous object recognition using local feature descriptors

Saliency detection and model-based tracking: a two part vision system for small robot navigation in forested environment

A Factor Graph Approach to Estimation and Model Predictive Control on Unmanned Aerial Vehicles

Differential dynamic programming for optimal estimation

Vistas and Wall-Floor Intersection Features: Enabling Autonomous Flight in Man-made Environments