8,544 research outputs found
Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality
We address the problem of interactively controlling the workspace of a mobile
robot to ensure a human-aware navigation. This is especially of relevance for
non-expert users living in human-robot shared spaces, e.g. home environments,
since they want to keep the control of their mobile robots, such as vacuum
cleaning or companion robots. Therefore, we introduce virtual borders that are
respected by a robot while performing its tasks. For this purpose, we employ a
RGB-D Google Tango tablet as human-robot interface in combination with an
augmented reality application to flexibly define virtual borders. We evaluated
our system with 15 non-expert users concerning accuracy, teaching time and
correctness and compared the results with other baseline methods based on
visual markers and a laser pointer. The experimental results show that our
method features an equally high accuracy while reducing the teaching time
significantly compared to the baseline methods. This holds for different border
lengths, shapes and variations in the teaching process. Finally, we
demonstrated the correctness of the approach, i.e. the mobile robot changes its
navigational behavior according to the user-defined virtual borders.Comment: Accepted on 2018 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), supplementary video: https://youtu.be/oQO8sQ0JBR
Perception for detection and grasping
The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft
Mixed marker-based/marker-less visual odometry system for mobile robots
When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test
MOMA: Visual Mobile Marker Odometry
In this paper, we present a cooperative odometry scheme based on the
detection of mobile markers in line with the idea of cooperative positioning
for multiple robots [1]. To this end, we introduce a simple optimization scheme
that realizes visual mobile marker odometry via accurate fixed marker-based
camera positioning and analyse the characteristics of errors inherent to the
method compared to classical fixed marker-based navigation and visual odometry.
In addition, we provide a specific UAV-UGV configuration that allows for
continuous movements of the UAV without doing stops and a minimal
caterpillar-like configuration that works with one UGV alone. Finally, we
present a real-world implementation and evaluation for the proposed UAV-UGV
configuration
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