Vision and Learning for Deliberative Monocular Cluttered Flight

Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available

Towards Visual Ego-motion Learning in Robots

Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped ego-motion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures, 2 table

Real-time kinematics for accurate geolocalization of images in telerobotic applications

The paper discusses a real-time kinematic system for accurate geolocalization of images, acquired though stereoscopic cameras mounted on a robot, particularly a teleoperated machinery. A teleoperated vehicle may be used to explore an unsafe environment and to acquire in real-time stereoscopic images through two cameras mounted on top of it. Each camera has a visible image sensor. For night operation, or in case temperature is an important parameter, each camera can be equipped with both visible and infrared image sensors. One of the main issues for telerobotic is the real-time and accurate geolocalization of the images, where an accuracy of few cm is required. Such value is much better than that that provided by GPS (Global Positioning System), which is in the order of few meters. To this aim, a real-time kinematic system is proposed which acquires the GPS signal of the vehicle, plus through an RF channel, the GPS signal of a reference base station, geolocalized with a cm-accuracy. To improve the robustness of the differential GPS system, also the data of an Inertial Measurement Unit are used. Another issue addressed in this paper is the real-time implementation of a stereoscopic image-processing algorithm to recover the 3D structure of the scene. The focus is on the 3D reconstruction of the scene to have the reference trajectory for the actuation done by a robotic arm with a proper end-effector

PROBE-GK: Predictive Robust Estimation using Generalized Kernels

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'16), Stockholm, Sweden, May 16-21, 201

Real-time smart and standalone vision/IMU navigation sensor

In this paper, we present a smart, standalone, multi-platform stereo vision/IMU-based navigation system, providing ego-motion estimation. The real-time visual odometry algorithm is run on a nano ITX single-board computer (SBC) of 1.9 GHz CPU and 16-core GPU. High-resolution stereo images of 1.2 megapixel provide high-quality data. Tracking of up to 750 features is made possible at 5 fps thanks to a minimal, but efficient, features detection–stereo matching–feature tracking scheme runs on the GPU. Furthermore, the feature tracking algorithm benefits from assistance of a 100 Hz IMU whose accelerometer and gyroscope data provide inertial features prediction enhancing execution speed and tracking efficiency. In a space mission context, we demonstrate robustness and accuracy of the real-time generated 6-degrees-of-freedom trajectories from our visual odometry algorithm. Performance evaluations are comparable to ground truth measurements from an external motion capture system