Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Drag-reduction strategies in wall-bounded turbulent flows using deep reinforcement learning

In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. In order to perform this study, we implement and describe in detail the reinforcement-learning interface to a computationally-efficient, parallelized, high-fidelity solver for fluid-flow simulations. We consider opposition control (Choi, Moin, and Kim, Journal of Fluid Mechanics 262, 1994) and the policies learnt using deep reinforcement learning (DRL) based on the state of the flow at two inner-scaled locations ($y^+ = 10$ and $y^+ = 15$). By using deep deterministic policy gradient (DDPG) algorithm, we are able to discover control strategies that outperform existing control methods. This represents a first step in the exploration of the capability of DRL algorithm to discover effective drag-reduction policies using information from different locations in the flow.Comment: 6 pages, 5 figure

Exponential martingales and changes of measure for counting processes

We give sufficient criteria for the Dol\'eans-Dade exponential of a stochastic integral with respect to a counting process local martingale to be a true martingale. The criteria are adapted particularly to the case of counting processes and are sufficiently weak to be useful and verifiable, as we illustrate by several examples. In particular, the criteria allow for the construction of for example nonexplosive Hawkes processes as well as counting processes with stochastic intensities depending on diffusion processes

Dynamic Characteristics of Bubbling and Turbulent Fluidization Using Hurst Analysis Technique

A non-intrusive vibration monitoring technique was used to study the flow behavior in a fluidized bed. This technique has several advantages compared to other techniques, such as pressure probes and optical fiber probes which may influence the measurement because they are intrusive. Experiments were conducted in a 15 cm diameter by 2 m tall fluidized bed using 470 micron sand particles. Auto correlation functions, mutual information function and Hurst exponent analyses were used to analyze the fluidized bed hydrodynamics near the transition point from bubbling to turbulent fluidization regime. These methods were able to detect the regime transition point using vibration signals

Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks

A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow. The training data are generated by performing a direct numerical simulation (DNS) at a friction Reynolds number of $Re_{\tau}=180$. Various networks are trained for predictions at three inner-scaled locations ($y^+ = 15,~30,~50$) and for different time steps between input samples $\Delta t^{+}_{s}$. The inherent non-linearity of the neural-network model enables a better prediction capability than linear methods, with a lower error in both the instantaneous flow fields and turbulent statistics. Using a dataset with higher $\Delta t^+_{s}$ improves the generalization at all the considered wall-normal locations, as long as the network capacity is sufficient to generalize over the dataset. The use of a multiple-output network, with parallel dedicated branches for two wall-normal locations, does not provide any improvement over two separated single-output networks, other than a moderate saving in training time. Training time can be effectively reduced, by a factor of 4, via a transfer learning method that initializes the network parameters using the optimized parameters of a previously-trained network

Application of low transformation-temperature filler to reduce the residual stresses in welded component

Tensile residual stress is a major issue in integrity of the welded structures. Undesirable tensile residual stress in welding may reduce fracture toughness and fatigue life of welded structures. The low transformation-temperature (LTT) fillers, due to introducing compressive residual stresses caused by prior martensitic transformation, can reduce tensile residual stresses in the weld zone. The effects of using LTT fillers on welding residual stresses of high strength steel sheets are studied and compared with conventional fillers. 3D finite element simulations including coupled thermal-metallurgical-mechanical analyses are developed using SYSWELD software to predict the welding residual stresses. For validation of the finite element model, the residual stresses are measured through hole drilling strain gage method. The results indicate that using the LTT fillers cause a decrease of the longitudinal tensile residual stresses of the weld metal from 554 MPa to 216 MPa in comparison with conventional fillers. The transverse residual stresses of the weld line are changed from tensile 156 MPa to compressive 289 MPa with using LTT fillers instead of conventional fillers

Snapshot observation of the physical structure and stratification in deep-water of the South Caspian Sea (western part)

Abstract. The physical parameters structures and different water masses using CTD measurements in southwestern part of the Caspian Sea (CS) adjacent to Anzali Port (AP) are investigated. CTD profiles were conducted along a transect perpendicular to the coastline on 13 stations from the coast down to 720 m on 22 January 2008. According to the results the continental shelf waters are located in the surface mixed layer. The surface mixed layer extends itself down to almost 100 m in outer areas of the continental shelf with a weak seasonal thermocline layer between 80 to 140 m Freshwaters inflow of local rivers is clearly seen outside the continental shelf at the surface layers. Investigating the dissolved oxygen reveals that winter convection is traceable down to 500 m in the lateral waters over the shelf break. Among the deeper stations that are located in continental rise and abyssal plain, 300 m seems to be a threshold for penetration of seasonal changes; therefore deeper waters tend to be impermeable against seasonal variances. Despite the small variations, stability is positive in the study area and temperature plays an important role in static stability and in triggering the lateral mixing. In view of both temperature-salinity and temperature-oxygen distributions in the southwestern part of the CS, two different water masses are separable in cold phase. Snapshot observations of physical properties in the early winter 2008, to some extent revealed that a mixing was triggered at least in the lateral waters of the study area. </jats:p