Konstantin Schauwecker

On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV

We present a quadrotor Micro Aerial Vehicle (MAV) equipped with four cameras, which are arranged in two stereo configurations. The MAV is able to perform stereo matching for each camera pair on-board and in real-time, using an efficient sparse stereo method. In case of the camera pair that is facing forward, the stereo matching results are used for a reduced stereo SLAM system. The other camera pair, which is facing downwards, is used for ground plane detection and tracking. Hence, we are able to obtain a full 6DoF pose estimate from each camera pair, which we fuse with inertial measurements in an extended Kalman filter. Special care is taken to compensate various drift errors. In an evaluation we show that using two instead of one camera pair significantly increases the pose estimation accuracy and robustness.

Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision

This paper presents a novel solution for micro aerial vehicles (MAVs) to autonomously search for and land on an arbitrary landing site using real-time monocular vision. The autonomous MAV is provided with only one single reference image of the landing site with an unknown size before initiating this task. We extend a well-known monocular visual SLAM algorithm that enables autonomous navigation of the MAV in unknown environments, in order to search for such landing sites. Furthermore, a multi-scale ORB feature based method is implemented and integrated into the SLAM framework for landing site detection. We use a RANSAC-based method to locate the landing site within the map of the SLAM system, taking advantage of those map points associated with the detected landing site. We demonstrate the efficiency of the presented vision system in autonomous flights, both indoor and in challenging outdoor environment.

Markerless Visual Control of a Quad-Rotor Micro Aerial Vehicle by Means of On-Board Stereo Processing

We present a quad-rotor micro aerial vehicle (MAV) that is capable to fly andnavigateautonomouslyinanunknownenvironment.Theonlysensoryinputused by the MAV are the imagery from two cameras in a stereo configuration, and data from an inertial measurement unit. We apply a fast sparse stereo matching algorithm incombinationwithavisualodometrymethodbasedonPTAMtoestimatethecurrent MAVpose,whichwerequireforautonomouscontrol.Allprocessingisperformedon a single board computer on-board the MAV. To our knowledge, this is the first MAV thatusesstereovisionfornavigation,anddoesnotrelyonvisualmarkersoroff-board processing. In aflight experiment, the MAV was capable to hover autonomously, and it was able to estimate its current position at a rate of 29Hz and with an average error of only 2.8cm.

On-board dual-stereo-vision for autonomous quadrotor navigation

We present a quadrotor Micro Aerial Vehicle (MAV) capable of autonomous indoor navigation. The MAV is equipped with four cameras arranged in two stereo configurations. One camera pair is facing forward and serves as input for a reduced stereo SLAM system. The other camera pair is facing downwards and is used for ground plane detection and tracking. All processing, including sparse stereo matching, is run on-board in real-time and at high processing rates. We demonstrate the capabilities of this MAV design in several flight experiments. Our MAV is able to recover from pose estimation errors and can cope with processing failures for one camera pair. We show that by using two camera pairs instead of one, we are able to significantly increase navigation accuracy and robustness.

A new feature detector and stereo matching method for accurate high-performance sparse stereo matching

Hardware platforms with limited processing power are often incapable of running dense stereo analysis algorithms at acceptable speed. Sparse algorithms provide an alternative but generally lack in accuracy. To overcome this predicament, we present an efficient sparse stereo analysis algorithm that applies a dense consistency check, leading to accurate matching results. We further improve matching accuracy by introducing a new feature detector based on FAST, which exhibits a less clustered feature distribution. The new feature detector leads to a superior performance of our stereo analysis algorithm. Performance evaluation shows that the proposed stereo matching system achieves processing rates above 200 frames per second on a commodity dual core CPU, and faster than video frame-rate processing on a low-performance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms.

Onboard monocular vision for landing of an MAV on a landing site specified by a single reference image

This paper presents a real-time monocular vision solution for MAVs to autonomously search for and land on an arbitrary landing site. The autonomous MAV is provided with only one single reference image of the landing site with an unknown size before initiating this task. To search for such landing sites, we extend a well-known visual SLAM algorithm that enables autonomous navigation of the MAV in unknown environments. A multi-scale ORB feature based method is implemented and integrated into the SLAM framework for landing site detection. We use a RANSAC-based method to locate the landing site within the map of the SLAM system, taking advantage of those map points associated with the detected landing site. We demonstrate the efficiency of the presented vision system in autonomous flight, and compare its accuracy with ground truth data provided by an external tracking system.

Advance in vision-based driver assistance

Vision-based driver assistance is an active safety measure currently under development in various car companies and research institutes worldwide. The paper informs about related activities at The University of Auckland, focussing on stereo vision, performance evaluation, provided test data, and currently developed components.

A comparative study of stereo-matching algorithms for road-modeling in the presence of windscreen wipers

In this study we examine three road-modeling methods, which we evaluate on seven stereo matching algorithms. The road-modeling methods we consider are a B-spline modeling technique based on region-growing and two versions of the popular v-disparity approach. The used stereo algorithms are variations or different parameterizations of belief propagation, graph cut and semi-global matching.

A comparative study of two vertical road modelling techniques

Binocular vision combined with stereo matching algorithms can be used in vehicles to gather data of the spatial proximity. To utilize this data we propose a new method for modeling the vertical road profile from a disparity map. This method is based on a region-growing technique, which iteratively performs a least-squares fit of a B-spline curve to a region of selected points. We compare this technique to two variants of the v-disparity method using either an envelope function or a planarity assumption. Our findings are that the proposed road-modeling technique outperforms both variants of the v-disparity technique, for which the planarity assumption is slightly better than the envelope version.

Current work in multimedia imaging at UoA's Tamaki campus

This report informs about current activities and results in the .enpeda‥ (short for ‘environment perception and driver assistance’) project and related performance evaluation studies, in panoramic visualization, in environmental surveillance based on scanned footprints of small species, in artistic filters, and in the design of efficient geometric algorithms for areas related to 2D or 3D imaging or robotics. The report summarizes some of the current work in multimedia imaging at Tamaki campus; see [52] for a previous report and further areas of research.