Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at https

This paper describes the dynamic window a p proach t o reactive collision avoidance for mobile robots equipped with synchro-drives. The a p proach i s d erived directly from the motion dynamics of the robot and i s t herefore particularly well-suited for robots o perating a t high speed. It di ers from previous approaches in that t he search for commands controlling t he translational a n d rotational velocity o f t he robot is carried out directly in the space of velocities. The advantage of our approach i s t hat it correctly and in an elegant w ay incorporatesthe dynamics of the robot. This is done by r e d ucing t he search space to t he dynamic window, which consists o f t he v elocities reachable within a short time i n terval. Within the dynamic window t he a p proach only considers admissible velocities yielding a trajectory on which t he robot is able to s t op safely. Among t hesevelocities the combination of translational and rotational velocity is chosen by m aximizing an objective f u nction. The objective f u nction includes a measure of progress towards a goal location, the forward velocity o f t he robot, and the d i s t ance to t he n ext obstacle on the trajectory. In extensive experiments t he approach presented here has been found t o safely control our mobile robot RHINO with speeds of up to 95 cm sec, in populated and dynamic environments.

Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinectstyle) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results.

Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called Mixture-MCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.