3D Modeling of Objects by Using Resilient Neural Network

Camera Calibration (CC) is a fundamental issue for Shape-Capture, Robotic-Vision and 3D Reconstruction in Photogrammetry and Computer Vision. The purpose of CC is the determination of the intrinsic parameters of cameras for metric evaluation of the images. Classical CC methods comprise of taking images of objects with known geometry, extracting the features of the objects from the images, and minimizing their 3D backprojection errors. In this paper, a novel implicit-CC model (CC-RN) based on Resilient Neural Networks has been introduced. The CC-RN is particularly useful for 3D reconstruction of the applications that do not require explicitly computation of physical camera parameters in addition to the expert knowledge. The CC-RN supports intelligent-photogrammetry, photogrammetron. In order to evaluate the success of the proposed implicit-CC model, the 3D reconstruction performance of the CC-RN has been compared with two different well-known implementations of the Direct Linear Transformation (DLT). Extensive simulation results show that the CC-RN achieves a better performance than the well-known DLTs in the 3D backprojection of scene

3D Modeling of Objects by Using Resilient Neural Network

Camera Calibration (CC) is a fundamental issue for Shape-Capture, Robotic-Vision and 3D Reconstruction in Photogrammetry and Computer Vision. The purpose of CC is the determination of the intrinsic parameters of cameras for metric evaluation of the images. Classical CC methods comprise of taking images of objects with known geometry, extracting the features of the objects from the images, and minimizing their 3D backprojection errors. In this paper, a novel implicit-CC model (CC-RN) based on Resilient Neural Networks has been introduced. The CC-RN is particularly useful for 3D reconstruction of the applications that do not require explicitly computation of physical camera parameters in addition to the expert knowledge. The CC-RN supports intelligent-photogrammetry, photogrammetron. In order to evaluate the success of the proposed implicit-CC model, the 3D reconstruction performance of the CC-RN has been compared with two different well-known implementations of the Direct Linear Transformation (DLT). Extensive simulation results show that the CC-RN achieves a better performance than the well-known DLTs in the 3D backprojection of scene

Body models in humans, animals, and robots: mechanisms and plasticity

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent - yet, as is so often the case, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. In the biological realm, evidence has been accumulated by diverse disciplines giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables to control the machine. In this article I compare the character of body representations in biology with their robotic counterparts and relate that to the differences in performance that we observe. I put forth a number of axes regarding the nature of such body models: fixed vs. plastic, amodal vs. modal, explicit vs. implicit, serial vs. parallel, modular vs. holistic, and centralized vs. distributed. An interesting trend emerges: on many of the axes, there is a sequence from robot body models, over body image, body schema, to the body representation in lower animals like the octopus. In some sense, robots have a lot in common with Ian Waterman - "the man who lost his body" - in that they rely on an explicit, veridical body model (body image taken to the extreme) and lack any implicit, multimodal representation (like the body schema) of their bodies. I will then detail how robots can inform the biological sciences dealing with body representations and finally, I will study which of the features of the "body in the brain" should be transferred to robots, giving rise to more adaptive and resilient, self-calibrating machines.Comment: 27 pages, 8 figure

A group-theoretic approach to formalizing bootstrapping problems

The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. In this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constraint of having "no prior information" can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commands. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-world range-finder and camera data from publicly available datasets

Markerless visual servoing on unknown objects for humanoid robot platforms

To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape. In this work we propose a framework for markerless visual servoing on unknown objects, which is divided in four main parts: I) a least-squares minimization problem is formulated to find the volume of the object graspable by the robot's hand using its stereo vision; II) a recursive Bayesian filtering technique, based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose (position and orientation) of the robot's end-effector without the use of markers; III) a nonlinear constrained optimization problem is formulated to compute the desired graspable pose about the object; IV) an image-based visual servo control commands the robot's end-effector toward the desired pose. We demonstrate effectiveness and robustness of our approach with extensive experiments on the iCub humanoid robot platform, achieving real-time computation, smooth trajectories and sub-pixel precisions

Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand

Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape. The shape completion network is based on VQDIF and predicts spatial occupancy values at arbitrary query points. As grasp predictor, we use our two-stage architecture that first generates hand poses using an autoregressive model and then regresses finger joint configurations per pose. Critical factors turn out to be sufficient data realism and augmentation, as well as special attention to difficult cases during training. Experiments on a physical robot platform demonstrate successful grasping of a wide range of household objects based on a depth image from a single viewpoint. The whole pipeline is fast, taking only about 1 s for completing the object's shape (0.7 s) and generating 1000 grasps (0.3 s).Comment: 8 pages, 10 figures, 3 tables, 1 algorithm, 2023 IEEE-RAS International Conference on Humanoid Robots (Humanoids), Project page: https://dlr-alr.github.io/2023-humanoids-completio

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved