0000000001314783

AUTHOR

Atle Aalerud

Industrial Environment Mapping Using Distributed Static 3D Sensor Nodes

This paper presents a system architecture for mapping and real-time monitoring of a relatively large industrial robotic environment of size 10 m × 15 m × 5 m. Six sensor nodes with embedded computing power and local processing of the 3D point clouds are placed close to the ceiling. The system architecture and data processing is based on the Robot Operating System (ROS) and the Point Cloud Library (PCL). The 3D sensors used are the Microsoft Kinect for Xbox One and point cloud data is collected at 20 Hz. A new manual calibration procedure is developed using reflective planes. The specified range of the used sensor is 0.8 m to 4.2 m, while depth data up to 9 m is used in this paper. Despite t…

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Dynamic Augmented Kalman Filtering for Human Motion Tracking under Occlusion Using Multiple 3D Sensors

In this paper real-time human motion tracking using multiple 3D sensors has been demonstrated in a relatively large industrial robot work cell. The proposed solution extends state-of-the-art by augmenting the constant velocity model and Kalman filter with low-pass filtered velocity states. The presented method is able to handle occlusions by dynamically inclusion in the Kalman filter of only those 3D sensors which provide valid human position data. Human motion tracking was achieved at a frame rate of 20 Hz, with a typical delay of 50 ms to 100 ms and an estimation accuracy of typically 0.10 m to 0.15 m.

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Scalability of GPU-Processed 3D Distance Maps for Industrial Environments

This paper contains a benchmark analysis of the open source library GPU-Voxels together with the Robot Operating System (ROS) in large-scale industrial robotics environment. Six sensor nodes with embedded computing generate real-time point cloud data as ROS topics. The overall data from all sensor nodes is processed by a combination of CPU and GPU on a central ROS node. Experimental results demonstrate that the system is able to handle frame rates of 10 and 20 Hz with voxel sizes of 4, 6, 8 and 12 cm without saturation of the CPU or the GPU used by the GPU-Voxels library. The results in this paper show that ROS, in combination with GPU-Voxels, can be used as a viable solution for real-time …

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Automatic Calibration of an Industrial RGB-D Camera Network Using Retroreflective Fiducial Markers

This paper describes a non-invasive, automatic, and robust method for calibrating a scalable RGB-D sensor network based on retroreflective ArUco markers and the iterative closest point (ICP) scheme. We demonstrate the system by calibrating a sensor network comprised of six sensor nodes positioned in a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m . Here, the automatic calibration achieved an average Euclidean error of 3 c m at distances up to 9.45 m . To achieve robustness, we apply several innovative techniques: Firstly, we mitigate the ambiguity problem that occurs when detecting a marker at long range or low resolution by comparing the…

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Real-time human collision detection for industrial robot cells

A collision detection system triggering on human motion was developed using the Robot Operating System (ROS) and the Point Cloud Library (PCL). ROS was used as the core of the programs and for the communication with an industrial robot. Combining the depths fields from the 3D cameras was accomplished by the use of PCL. The library was also the underlying tool for segmenting the human from the registrated point clouds. Benchmarking of several collision algorithms was done in order to compare the solution. The registration process gave satisfactory results when testing the repetitiveness and the accuracy of the implementation. The segmentation algorithm was able to segment a person represente…

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Evaluation of Perception Latencies in a Human-Robot Collaborative Environment

The latency in vision-based sensor systems used in human-robot collaborative environments is an important safety parameter which in most cases has been neglected by researchers. The main reason for this neglect is the lack of an accurate ground-truth sensor system with a minimal delay to benchmark the vision-sensors against. In this paper the latencies of 3D vision-based sensors are experimentally evaluated and analyzed using an accurate laser-tracker system which communicates on a dedicated EtherCAT channel with minimal delay. The experimental results in the paper demonstrate that the latency in the vision-based sensor system is many orders higher than the latency in the control and actuat…

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Reshaping Field of View and Resolution with Segmented Reflectors: Bridging the Gap between Rotating and Solid-State LiDARs

This paper describes the first simulations and experimental results of a novel segmented Light Detection And Ranging (LiDAR) reflector. Large portions of the rotating LiDAR data are typically discarded due to occlusion or a misplaced field of view (FOV). The proposed reflector solves this problem by reflecting the entire FOV of the rotating LiDAR towards a target. Optical simulation results, using Zemax OpticStudio, suggest that adding a reflector reduces the range of the embedded LiDAR with only 3 . 9 . Furthermore, pattern simulation results show that a radially reshaped FOV can be configured to maximize point cloud density, maximize coverage, or a combination. Here, the maximum density i…

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Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments

This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m &times

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Industrial Perception of a Human World : A Hitchhiker's Guide to Autonomy

For several decades, industrial automation has helped companies save money and increase workplace safety so that many repetitive and dangerous tasks may be performed in a cost-efficient and secure manner. These systems typically follow a sense-plan-act scheme where sensors provide processed information to select the proper actions. However, without perception, motion control actions are limited to operating in a known static environment without dynamic obstacles. Consequently, the single most significant shortcoming to achieving a higher level of autonomy in cluttered dynamic environments, for instance drilling, crane operations, logistics, and robotics, is to solve scene understanding base…

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Visual Marker Guided Point Cloud Registration in a Large Multi-Sensor Industrial Robot Cell

This paper presents a benchmark and accuracy analysis of 3D sensor calibration in a large industrial robot cell. The sensors used were the Kinect v2 which contains both an RGB and an IR camera measuring depth based on the time-of-flight principle. The approach taken was based on a novel procedure combining Aruco visual markers, methods using region of interest and iterative closest point. The calibration of sensors is performed pairwise, exploiting the fact that time-of-flight sensors can have some overlap in the generated point cloud data. For a volume measuring 10m × 14m × 5m a typical accuracy of the generated point cloud data of 5–10cm was achieved using six sensor nodes.

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Replication Data for: Automatic Calibration of an Industrial RGB-D Camera Network using Retroreflective Fiducial Markers.

Replication Data in the form of a Robot Operating System (ROS) recording (ROS-bag) to replicate the results of the paper "Automatic Calibration of an Industrial RGB-D Camera Network using Retroreflective Fiducial Markers." The contents of the dataset are timestamped images and point clouds recorded from six different sensor nodes.

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