Supporting Uavs in Low Visibility Conditions by Multiple-Pulse Laser Scanning Devices

Unmanned Aerial Vehicles (UAVs) are nowadays promising platforms for capturing spatial information, because they are low cost solutions, which are easy to bring to the surveying field and can operate automatically. Usually these devices are equipped with visual sensors to support the navigation of the platform or to transmit observations of the environment to the operator. By collecting the data and processing the captured images even an estimation of the observed environment in form of 3D information is available. Therefore Simultaneous Localization and Mapping (SLAM) algorithms are well known for processing data which is captured in the visible domain. However, situations can occur where gathering visual information is difficult due to given limitations in form of low visibility. For example if soft obstacles in form of translucent materials are given in disaster scenarios with smoke and operating has still to be ensured, active optical sensors (e.g. laser scanners) are gaining interest because they can penetrate the soft obstacle and allow to acquire information behind it. A new lightweight (210 g), simplified and minimized scanning unit is now available which allows to capture multiple reflections for each transmitted laser pulse, namely the Hokuyo UTM-30LX-EW. With such a device, it is possible to overcome the above mentioned restrictions or limitations of low visibility by soft obstacles and even measure under critical circumstances. A multi-pulse system can provide accurate measurements on, within, and behind the soft obstacle. This research focuses on investigating the ability and performance of a laser scanner to penetrate the soft obstacle. Thus, investigations on a system that overcomes these limitations and provides a solution will be given. First promising experimental results considering soft obstacle are described

3D Density-Gradient based Edge Detection on Neural Radiance Fields (NeRFs) for Geometric Reconstruction

Generating geometric 3D reconstructions from Neural Radiance Fields (NeRFs) is of great interest. However, accurate and complete reconstructions based on the density values are challenging. The network output depends on input data, NeRF network configuration and hyperparameter. As a result, the direct usage of density values, e.g. via filtering with global density thresholds, usually requires empirical investigations. Under the assumption that the density increases from non-object to object area, the utilization of density gradients from relative values is evident. As the density represents a position-dependent parameter it can be handled anisotropically, therefore processing of the voxelized 3D density field is justified. In this regard, we address geometric 3D reconstructions based on density gradients, whereas the gradients result from 3D edge detection filters of the first and second derivatives, namely Sobel, Canny and Laplacian of Gaussian. The gradients rely on relative neighboring density values in all directions, thus are independent from absolute magnitudes. Consequently, gradient filters are able to extract edges along a wide density range, almost independent from assumptions and empirical investigations. Our approach demonstrates the capability to achieve geometric 3D reconstructions with high geometric accuracy on object surfaces and remarkable object completeness. Notably, Canny filter effectively eliminates gaps, delivers a uniform point density, and strikes a favorable balance between correctness and completeness across the scenes.Comment: 8 pages, 4 figures, 2 tables. Will be published in the ISPRS The International Archives of Photogrammetry, Remote Sensing and Spatial Information Science

Impact of different trajectories on extrinsic self-calibration for vehicle-based mobile laser scanning systems

The trend toward further integration of automotive electronic control units functionality into domain control units as well as the rise of computing-intensive driver assistance systems has led to a demand for high-performance automotive computation platforms. These platforms have to fulfill stringent safety requirements. One promising approach is the use of performance computation units in combination with safety controllers in a single control unit. Such systems require adequate communication links between the computation units. While Ethernet is widely used, a high-speed serial link communication protocol supported by an Infineon AURIX safety controller appears to be a promising alternative. In this paper, a high-speed serial link IP core is presented, which enables this type of high-speed serial link communication interface for field-programmable gate array–based computing units. In our test setup, the IP core was implemented in a high-performance Xilinx Zynq UltraScale+, which communicated with an Infineon AURIX via high-speed serial link and Ethernet. The first bandwidth measurements demonstrated that high-speed serial link is an interesting candidate for inter-chip communication, resulting in bandwidths reaching up to 127 Mbit/s using stream transmissions

Automatic Real-Time Pose Estimation of Machinery from Images

The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than [Formula: see text] with the translation components and accuracies higher than [Formula: see text] with the rotation components. As a result, 3D point accuracies higher than [Formula: see text] were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors