Gearing up and accelerating cross-fertilization between academic and industrial robotics research in Europe - Technology transfer experiments from the ECHORD project, Springer Tracts in Advanced Robotics (STAR), 2013
Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This ... more Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This year we used a heterogeneous team, consisting of improved differential drive robots and new omnidirectional robots. We designed new electronics and added prediction and path planning to the behavior control. Our team won fourth place in the SmallSize league competition.
Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be per... more Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many distractors, often in restricted spaces. To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic datasets possible. We evaluate our approach on two challenging datasets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task; and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception.
Object class segmentation is a computer vision task which requires labeling each pixel of an imag... more Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results.
Gearing up and accelerating cross-fertilization between academic and industrial robotics research in Europe - Technology transfer experiments from the ECHORD project, Springer Tracts in Advanced Robotics (STAR), 2013
Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This ... more Our F180 team, the FU-Fighters, participated for the third time at the RoboCup competition. This year we used a heterogeneous team, consisting of improved differential drive robots and new omnidirectional robots. We designed new electronics and added prediction and path planning to the behavior control. Our team won fourth place in the SmallSize league competition.
Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be per... more Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many distractors, often in restricted spaces. To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic datasets possible. We evaluate our approach on two challenging datasets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task; and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception.
Object class segmentation is a computer vision task which requires labeling each pixel of an imag... more Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results.
Uploads
Papers by Sven Behnke