Furniture models learned from the WWW: using web catalogs to locate and categorize unknown furniture pieces in 3D laser scans

In this article, we investigate how autonomous robots can exploit the high quality information already available from the WWW concerning 3-D models of office furniture. Apart from the hobbyist effort in Google 3-D Warehouse, many companies providing office furnishings already have the models for considerable portions of the objects found in our workplaces and homes. In particular, we present an approach that allows a robot to learn generic models of typical office furniture using examples found in the Web. These generic models are then used by the robot to locate and categorize unknown furniture in real indoor environments

Intelligent Robotic Perception Systems

Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics

Towards Modular Spatio-temporal Perception for Task-adapting Robots

In perception systems for object recognition, the advantage of multiple modalities, of combining approaches, and several views is emphasized, as they improve accuracy. However, there are great variances in the implementation, suggesting that there is no consensus yet on how to approach this problem. Nonetheless, we can identify some common features of the methods and propose a flexible system where existing and future approaches can be tested, compared and combined. We present a modular system in which perception routines can be easily added, and define the logic of making them work together based on the lessons learned from different experiments

Search for signatures of dust in the Pluto-Charon system using Herschel/PACS observations

In this letter we explore the environment of Pluto and Charon in the far infrared with the main aim to identify the signs of any possible dust ring, should it exist in the system. Our study is based on observations performed at 70 um with the PACS instrument onboard the Herschel Space Observatory at 9 epochs between March 14 and 19, 2012. The far-infrared images of the Pluto-Charon system are compared to those of the point spread function (PSF) reference quasar 3C454.3. The deviation between the observed Pluto-Charon and reference PSFs are less then 1 sigma indicating that clear evidence for an extended dust ring around the system was not found. Our method is capable of detecting a hypothetical ring with a total flux of ~3.3 mJy at a distance of ~153 000 km (~8.2 Pluto-Charon distances) from the system barycentre. We place upper limits on the total disk mass and on the column density in a reasonable disk configuration and analyse the hazard during the flyby of NASAs New Horizons in July 2015. This realistic model configuration predicts a column density of 8.7x10^(-10) gcm^(-2) along the path of the probe and an impactor mass of 8.7x10^(-5) g.Comment: 10 pages, 4 figures, 2 table

Cumulative object categorization in clutter

In this paper we present an approach based on scene- or part-graphs for geometrically categorizing touching and occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks

Towards Modular Spatio-temporal Perception for Task-adapting Robots

In perception systems for object recognition, the advantage of multiple modalities, of combining approaches, and several views is emphasized, as they improve accuracy. However, there are great variances in the implementation, suggesting that there is no consensus yet on how to approach this problem. Nonetheless, we can identify some common features of the methods and propose a flexible system where existing and future approaches can be tested, compared and combined. We present a modular system in which perception routines can be easily added, and define the logic of making them work together based on the lessons learned from different experiments

Effect of drought on yield components of maize hybrids (Zea mays L)

When investigating drought tolerance, it must not be forgotten that drought stress is a complex phenomenon ex¬hibiting quite different characters in different years and locations. For this reason, the plant response to drought is also a complex process. In our study, 83 maize hybrids originating from various countries were investigated over a period of two years, under irrigated and non-irrigated conditions. The drought tolerance of plants in the non-irrigated plots was analysed in terms of flowering synchrony and yield components. It could be concluded from the results that in response to long-term water deficit the period between tasselling and silking became longer, while the analysis of yield components revealed the greatest reductions in the number of kernels per ear and in the proportion of seed set. As the degree of proterandry increased, there was a decline in the grain yield, confirming that the analysis of this trait could be a way of predicting drought tolerance. Considerable differences in drought tolerance were observed between the genetic materials included in the analysis, suggesting the presence among these parental lines and hybrids of genotypes resistant to long-term water deficit, suitable for cultivation under dry conditions. An analysis of correlations between the traits revealed that proterandry should be treated as a priority trait when investigating drought stress tolerance, as better predictions can be made of both drought tolerance and potential yields, leading to more reliable selection for higher yields

Multi-Path Learning for Object Pose Estimation Across Domains

We introduce a scalable approach for object pose estima-tion trained on simulated RGB views of multiple 3D modelstogether. We learn an encoding of object views that doesnot only describe an implicit orientation of all objects seenduring training, but can also relate views of untrained ob-jects. Our single-encoder-multi-decoder network is trainedusing a technique we denote multi-path learning: Whilethe encoder is shared by all objects, each decoder only re-constructs views of a single object. Consequently, viewsof different instances do not have to be separated in thelatent space and can share common features. The result-ing encoder generalizes well from synthetic to real dataand across various instances, categories, model types anddatasets. We systematically investigate the learned encod-ings, their generalization, and iterative refinement strate-gies on the ModelNet40 and T-LESS dataset. Despite train-ing jointly on multiple objects, our 6D Object Detectionpipeline achieves state-of-the-art results on T-LESS at muchlower runtimes than competing approaches

Multi-path Learning for Object Pose Estimation Across Domains

We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, but can also relate views of untrained objects. Our single-encoder-multi-decoder network is trained using a technique we denote "multi-path learning": While the encoder is shared by all objects, each decoder only reconstructs views of a single object. Consequently, views of different instances do not have to be separated in the latent space and can share common features. The resulting encoder generalizes well from synthetic to real data and across various instances, categories, model types and datasets. We systematically investigate the learned encodings, their generalization, and iterative refinement strategies on the ModelNet40 and T-LESS dataset. Despite training jointly on multiple objects, our 6D Object Detection pipeline achieves state-of-the-art results on T-LESS at much lower runtimes than competing approaches.Comment: To appear at CVPR 2020; Code will be available here: https://github.com/DLR-RM/AugmentedAutoencoder/tree/multipat