| [1] | Ait Jellal, Radouane and Lange, Manuel and Wassermann, Benjamin and Schilling, Andreas and Zell, Andreas. LS-ELAS: Line Segment based Efficient Large Scale Stereo Matching. In IEEE International Conference on Robotics and Automation (ICRA), Singapore,may-jun, 2017. [pdf]Bibtex@INPROCEEDINGS{JellalICRA2017,title = {LS-ELAS: Line Segment based Efficient Large Scale Stereo Matching}, author = {Ait Jellal, Radouane and Lange, Manuel and Wassermann, Benjamin and Schilling, Andreas and Zell, Andreas}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2017}, address = {Singapore}, month = {may-jun}, abstract = {We present LS-ELAS, a line segment extension to the ELAS algorithm, which increases the performance and robustness. LS-ELAS is a binocular dense stereo matching algorithm, which computes the disparities in constant time for most of the pixels in the image and in linear time for a small subset of the pixels (support points). Our approach is based on line segments to determine the support points instead of uniformly selecting them over the image range. This way we find very informative support points which preserve the depth discontinuity. The prior of our Bayesian stereo matching method is based on a set of line segments and a set of support points. Both sets are plugged into a constrained Delaunay triangulation to generate a triangulation mesh which is aware of possible depth discontinuities. We further increased the accuracy by using an adaptive method to sample candidate points along edge segments.}, days = {29-3}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?JellalICRA2017}, owner = {jellal}, project = {abt_zell, robotics, stereo matching, quadcopteric}, timestamp = {2017.16.01}, pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2017/JellalICRA17.pdf}, } |
| [2] | Buck, Sebastian and Hanten, Richard and Bohlmann, Karsten and Zell, Andreas. Multi-sensor Payload Detection and Acquisition for Truck-Trailer AGVs. In Robotics and Automation (ICRA), 2017 IEEE International Conference on, Singapore,may-jun, 2017. Bibtex@INPROCEEDINGS{BuckICRA2017,title = {Multi-sensor Payload Detection and Acquisition for Truck-Trailer AGVs}, author = {Buck, Sebastian and Hanten, Richard and Bohlmann, Karsten and Zell, Andreas}, booktitle = {Robotics and Automation (ICRA), 2017 IEEE International Conference on}, address = {Singapore}, year = {2017}, month = {may-jun}, days = {29-3}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?BuckICRA2017}, owner = {buck}, project = {abt_zell, robotics, patsy, csapex, csnavigation}, } |
| [3] | Huski\'{c}, Goran and Buck, Sebastian and Zell, Andreas. Path Following Control of Skid-Steered Wheeled Mobile Robots at Higher Speeds on Different Terrain Types. In IEEE International Conference on Robotics and Automation (ICRA), Singapore,may-jun, 2017. Bibtex@INPROCEEDINGS{HuskicICRA2017,title = {Path Following Control of Skid-Steered Wheeled Mobile Robots at Higher Speeds on Different Terrain Types}, author = {Huski\'{c}, Goran and Buck, Sebastian and Zell, Andreas}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2017}, address = {Singapore}, month = {may-jun}, abstract = {A new nonlinear control law for path following with skid-steered mobile robots is proposed. A terrain dependent kinematic model is utilized in path coordinates, and the kinematic parameters are experimentally evaluated. A kinematic path following control is developed using the Lyapunov approach. A separate linear velocity control is then proposed, taking reachable curvatures and actuator saturation into account. The proposed approach is experimentally evaluated in different terrain scenarios, and compared with two other state-of-the-art algorithms. The skid-steered vehicle used for the experiments is the Robotnik Summit XL, a well known commercial mobile robot.}, days = {29-3}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?HuskicICRA2017}, owner = {huskic}, project = {abt_zell, robotics, path_following, control, csnavigation}, timestamp = {2017.16.01}, } |
| [4] | Jordan, Julian and Zell, Andreas. Real-time Pose Estimation on Elevation Maps for Wheeled Vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages ,, Vancouver, Canada,sep, 2017. Bibtex@INPROCEEDINGS{JordanIROS2017,title = {Real-time Pose Estimation on Elevation Maps for Wheeled Vehicles}, author = {Jordan, Julian and Zell, Andreas}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, address = {Vancouver, Canada}, pages = {}, year = {2017}, month = {sep}, abstract = {Fast and accurate obstacle detection is a crucial component for autonomous robot navigation. It becomes even more important for a shared control vehicle like an electric wheeled walker, since the safety of the vehicle and the user depend on the correct classification of obstacles. This work describes a method for pose estimation of four-wheeled vehicles, which utilizes the fixed resolution of digital elevation maps to generate a detailed vehicle model. The vehicle’s wheels are also approximated using digital elevation maps, allowing efficient calculation of wheel to ground contact points and therefore fast and accurate estimation of valid vehicle poses. To evaluate the proposed method, pose estimates are compared to three datasets including ground truth poses: one created using an external tracking system and two created by simulations of wheeled robots. It is also shown that the method is fast enough for real time operation.}, days = {24-28}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?JordanIROS2017}, owner = {jordan}, note = {}, project = {abt_zell, robotics, obstacle detection, model based, elevation map}, timestamp = {2017.10.27}, } |
| [5] | Jordan, Julian and Zell, Andreas. Kinematic Model based Visual Odometry for Differential Drive Vehicles. In IEEE 8th European Conference on Mobile Robots (ECMR), ENSTA ParisTech, Université Paris-Saclay, France,sept, 2017. Bibtex@INPROCEEDINGS{JordanECMR2017,title = {Kinematic Model based Visual Odometry for Differential Drive Vehicles}, author = {Jordan, Julian and Zell, Andreas}, booktitle = {IEEE 8th European Conference on Mobile Robots (ECMR)}, year = {2017}, address = {ENSTA ParisTech, Université Paris-Saclay, France}, month = {sept}, abstract = {This work presents KMVO, a ground plane based visual odometry that utilizes the vehicle’s kinematic model to improve accuracy and robustness. Instead of solving a generic image alignment problem, the motion parameters of a differential drive vehicle can be directly estimated from RGB- D image data. In addition, a method for outlier rejection is presented that can deal with large percentages of outliers. The system is designed to run in real time on a single thread of a mobile CPU. The results of the proposed method are compared to other publicly available visual odometry and SLAM methods on a set of nine real world image sequences of different indoor environments.}, days = {6-8}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?JordanECMR2017}, owner = {jordan}, project = {abt_zell, robotics, MobilAssist, Visual Odometry Direct image alignment, Kinematik Model}, note = {}, timestamp = {2017.10.07}, } |
| [6] | Ait Jellal, Radouane and Zell, Andreas. Outdoor Obstacle Avoidance based on Hybrid Stereo Visual SLAM for an Autonomous Quadrotor MAV. In IEEE 8th European Conference on Mobile Robots (ECMR), ENSTA ParisTech, Université Paris-Saclay, France,sept, 2017. [pdf]Bibtex@INPROCEEDINGS{JellalECMR2017,title = {Outdoor Obstacle Avoidance based on Hybrid Stereo Visual SLAM for an Autonomous Quadrotor MAV}, author = {Ait Jellal, Radouane and Zell, Andreas}, booktitle = {IEEE 8th European Conference on Mobile Robots (ECMR)}, year = {2017}, address = {ENSTA ParisTech, Université Paris-Saclay, France}, month = {sept}, abstract = {We address the problem of on-line volumentric map creation of unknown environments and planning of safe trajectories. The sensor used for this purpose is a stereo camera. Our system is designed to work in GPS denied areas. We design a keyframe based hybrid SLAM algorithm which combines feature-based stereo SLAM and direct stereo SLAM. We use it to grow the map while keeping track of the camera pose in the map. The SLAM system builds a sparse map. For the path planning we build a dense volumetric map by computing dense stereo matching at keyframes and inserting the point clouds in an Octomap. The computed disparity maps are reused on the direct tracking refinement step of our hybrid SLAM. Safe trajectories are then estimated using the RRTstar algorithm in the SE(3) state space. In our experiments, we show that we can map large environments with hundreds of keyframes. We also conducted autonomous outdoor flights using a quadcopter to validate our approach for obstacle avoidance.}, days = {6-8}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?JellalECMR2017}, owner = {jellal}, project = {abt_zell, robotics, stereo matching, Hybrid SLAM, Direct image alignment, Stereo SLAM, outdoor obstacle avoidance}, note = {}, timestamp = {2017.10.07}, pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2017/JellalECMR17.pdf}, } |
| [7] | Huski\'{c}, Goran and Buck, Sebastian and Ibargüen Gonz\'alez, Luis Azareel and Zell, Andreas. Person Following at Higher Speeds using a Skid-Steered Mobile Robot. In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on, pages ,, Vancouver, Canada,sep, 2017. Bibtex@INPROCEEDINGS{HuskicIROS2017,title = {Person Following at Higher Speeds using a Skid-Steered Mobile Robot}, author = {Huski\'{c}, Goran and Buck, Sebastian and Ibargüen Gonz\'alez, Luis Azareel and Zell, Andreas}, booktitle = {Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on}, address = {Vancouver, Canada}, pages = {}, year = {2017}, month = {sep}, days = {24-28}, detailspage = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/LiteraturDB/details.html?HuskicIROS2017}, owner = {huskic}, note = {}, project = {abt_zell, robotics, path_following, control, csnavigation}, } |
| [8] | Otte, Sebastian and Zwiener, Adrian and Butz, Martin V. Inherently Constraint-Aware Control of Many-Joint Robot Arms with Inverse Recurrent Models. In International Conference on Artificial Neural Networks, pages 262--270,2017. Bibtex@inproceedings{OtteICANN2017,title = {Inherently Constraint-Aware Control of Many-Joint Robot Arms with Inverse Recurrent Models}, author = {Otte, Sebastian and Zwiener, Adrian and Butz, Martin V}, booktitle = {International Conference on Artificial Neural Networks}, pages = {262--270}, year = {2017}, organization = {Springer}, project = {abt_zell, rnn}, } |
| [9] | Shaowu Yang and Sebastian A. Scherer and Xiaodong Yi and Andreas Zell. Multi-camera visual \{SLAM\} for autonomous navigation of micro aerial vehicles. , pages 116 - 134,2017. [url]Bibtex@article{Yang2017116,title = {Multi-camera visual \{SLAM\} for autonomous navigation of micro aerial vehicles}, journal = {Robotics and Autonomous Systems}, volume = {93}, number = {}, pages = {116 - 134}, year = {2017}, note = {}, issn = {0921-8890}, doi = {https://doi.org/10.1016/j.robot.2017.03.018}, url = {http://www.sciencedirect.com/science/article/pii/S0921889015302177}, author = {Shaowu Yang and Sebastian A. Scherer and Xiaodong Yi and Andreas Zell}, keywords = {Autonomous navigation}, project = {abt_zell, SLAM}, abstract = {Abstract In this paper, we present a visual simultaneous localization and mapping (SLAM) system which integrates measurements from multiple cameras to achieve robust pose tracking for autonomous navigation of micro aerial vehicles (MAVs) in unknown complex environments. We analyze the iterative optimizations for pose tracking and map refinement of visual \{SLAM\} in multi-camera cases. The analysis ensures the soundness and accuracy of each optimization update. A well-known monocular visual \{SLAM\} system is extended to utilize two cameras with non-overlapping fields of view (FOVs) in the final implementation. The resulting visual \{SLAM\} system enables autonomous navigation of an \{MAV\} in complex scenarios. The theory behind this system can easily be extended to multi-camera configurations, when the onboard computational capability allows this. For operations in large-scale environments, we modify the resulting visual \{SLAM\} system to be a constant-time robust visual odometry. To form a full visual \{SLAM\} system, we further implement an efficient back-end for loop closing. The back-end maintains a keyframe-based global map, which is also used for loop-closure detection. An adaptive-window pose-graph optimization method is proposed to refine keyframe poses of the global map and thus correct pose drift that is inherent in the visual odometry. We demonstrate the efficiency of the proposed visual \{SLAM\} system for applications onboard of \{MAVs\} in experiments with both autonomous and manual flights. The pose tracking results are compared with ground truth data provided by an external tracking system.}, } |