173 research outputs found
Sensor Observability Index: Evaluating Sensor Alignment for Task-Space Observability in Robotic Manipulators
In this paper, we propose a preliminary definition and analysis of the novel
concept of sensor observability index. The goal is to analyse and evaluate the
performance of distributed directional or axial-based sensors to observe
specific axes in task space as a function of joint configuration in serial
robot manipulators. For example, joint torque sensors are often used in serial
robot manipulators and assumed to be perfectly capable of estimating end
effector forces, but certain joint configurations may cause one or more
task-space axes to be unobservable as a result of how the joint torque sensors
are aligned. The proposed sensor observability provides a method to analyse the
quality of the current robot configuration to observe the task space. Parallels
are drawn between sensor observability and the traditional kinematic Jacobian
for the particular case of joint torque sensors in serial robot manipulators.
Although similar information can be retrieved from kinematic analysis of the
Jacobian transpose in serial manipulators, sensor observability is shown to be
more generalizable in terms of analysing non-joint-mounted sensors and other
sensor types. In addition, null-space analysis of the Jacobian transpose is
susceptible to false observability singularities. Simulations and experiments
using the robot Baxter demonstrate the importance of maintaining proper sensor
observability in physical interactions.Comment: 7 pages, 5 figures, conference pape
Self-supervised Vector-Quantization in Visual SLAM using Deep Convolutional Autoencoders
In this paper, we introduce AE-FABMAP, a new self-supervised bag of
words-based SLAM method. We also present AE-ORB-SLAM, a modified version of the
current state of the art BoW-based path planning algorithm. That is, we have
used a deep convolutional autoencoder to find loop closures. In the context of
bag of words visual SLAM, vector quantization (VQ) is considered as the most
time-consuming part of the SLAM procedure, which is usually performed in the
offline phase of the SLAM algorithm using unsupervised algorithms such as
Kmeans++. We have addressed the loop closure detection part of the BoW-based
SLAM methods in a self-supervised manner, by integrating an autoencoder for
doing vector quantization. This approach can increase the accuracy of
large-scale SLAM, where plenty of unlabeled data is available. The main
advantage of using a self-supervised is that it can help reducing the amount of
labeling. Furthermore, experiments show that autoencoders are far more
efficient than semi-supervised methods like graph convolutional neural
networks, in terms of speed and memory consumption. We integrated this method
into the state of the art long range appearance based visual bag of word SLAM,
FABMAP2, also in ORB-SLAM. Experiments demonstrate the superiority of this
approach in indoor and outdoor datasets over regular FABMAP2 in all cases, and
it achieves higher accuracy in loop closure detection and trajectory
generation
WGICP: Differentiable Weighted GICP-Based Lidar Odometry
We present a novel differentiable weighted generalized iterative closest
point (WGICP) method applicable to general 3D point cloud data, including that
from Lidar. Our method builds on differentiable generalized ICP (GICP), and we
propose using the differentiable K-Nearest Neighbor (KNN) algorithm to enhance
differentiability. The differentiable GICP algorithm provides the gradient of
output pose estimation with respect to each input point, which allows us to
train a neural network to predict its importance, or weight, in estimating the
correct pose. In contrast to the other ICP-based methods that use voxel-based
downsampling or matching methods to reduce the computational cost, our method
directly reduces the number of points used for GICP by only selecting those
with the highest weights and ignoring redundant ones with lower weights. We
show that our method improves both accuracy and speed of the GICP algorithm for
the KITTI dataset and can be used to develop a more robust and efficient SLAM
system.Comment: 6 page
Self-stabilising target counting in wireless sensor networks using Euler integration
Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.Postprin
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
Poles and building edges are frequently observable objects on urban roads,
conveying reliable hints for various computer vision tasks. To repetitively
extract them as features and perform association between discrete LiDAR frames
for registration, we propose the first learning-based feature segmentation and
description model for 3D lines in LiDAR point cloud. To train our model without
the time consuming and tedious data labeling process, we first generate
synthetic primitives for the basic appearance of target lines, and build an
iterative line auto-labeling process to gradually refine line labels on real
LiDAR scans. Our segmentation model can extract lines under arbitrary scale
perturbations, and we use shared EdgeConv encoder layers to train the two
segmentation and descriptor heads jointly. Base on the model, we can build a
highly-available global registration module for point cloud registration, in
conditions without initial transformation hints. Experiments have demonstrated
that our line-based registration method is highly competitive to
state-of-the-art point-based approaches. Our code is available at
https://github.com/zxrzju/SuperLine3D.git.Comment: 17 pages, ECCV 2022 Accepte
Distributed Monocular SLAM for Indoor Map Building
Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment
Semi-supervised Vector-Quantization in Visual SLAM using HGCN
In this paper, two semi-supervised appearance based loop closure detection
technique, HGCN-FABMAP and HGCN-BoW are introduced. Furthermore an extension to
the current state of the art localization SLAM algorithm, ORB-SLAM, is
presented. The proposed HGCN-FABMAP method is implemented in an off-line manner
incorporating Bayesian probabilistic schema for loop detection decision making.
Specifically, we let a Hyperbolic Graph Convolutional Neural Network (HGCN) to
operate over the SURF features graph space, and perform vector quantization
part of the SLAM procedure. This part previously was performed in an
unsupervised manner using algorithms like HKmeans, kmeans++,..etc. The main
Advantage of using HGCN, is that it scales linearly in number of graph edges.
Experimental results shows that HGCN-FABMAP algorithm needs far more cluster
centroids than HGCN-ORB, otherwise it fails to detect loop closures. Therefore
we consider HGCN-ORB to be more efficient in terms of memory consumption, also
we conclude the superiority of HGCN-BoW and HGCN-FABMAP with respect to other
algorithms
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
The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing
Robotic manipulation is currently undergoing a profound paradigm shift due to
the increasing needs for flexible manufacturing systems, and at the same time,
because of the advances in enabling technologies such as sensing, learning,
optimization, and hardware. This demands for robots that can observe and reason
about their workspace, and that are skillfull enough to complete various
assembly processes in weakly-structured settings. Moreover, it remains a great
challenge to enable operators for teaching robots on-site, while managing the
inherent complexity of perception, control, motion planning and reaction to
unexpected situations. Motivated by real-world industrial applications, this
paper demonstrates the potential of such a paradigm shift in robotics on the
industrial case of an e-Bike motor assembly. The paper presents a concept for
teaching and programming adaptive robots on-site and demonstrates their
potential for the named applications. The framework includes: (i) a method to
teach perception systems onsite in a self-supervised manner, (ii) a general
representation of object-centric motion skills and force-sensitive assembly
skills, both learned from demonstration, (iii) a sequencing approach that
exploits a human-designed plan to perform complex tasks, and (iv) a system
solution for adapting and optimizing skills online. The aforementioned
components are interfaced through a four-layer software architecture that makes
our framework a tangible industrial technology. To demonstrate the generality
of the proposed framework, we provide, in addition to the motivating e-Bike
motor assembly, a further case study on dense box packing for logistics
automation
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