48 research outputs found

    Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection

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    Delay/Doppler-Mapping GPS-Reflection Remote-Sensing System

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    A radio receiver system that features enhanced capabilities for remote sensing by use of reflected Global Positioning System (GPS) signals has been developed. This system was designed primarily for ocean altimetry, but can also be used for scatterometry and bistatic synthetic-aperture radar imaging. Moreover, it could readily be adapted to utilize navigation-satellite systems other than the GPS, including the Russian Global Navigation Satellite System GLONASS) and the proposed European Galileo system. This remote-sensing system offers both advantages and disadvantages over traditional radar altimeters: One advantage of GPS-reflection systems is that they cost less because there is no need to transmit signals. Another advantage is that there are more simultaneous measurement opportunities - one for each GPS satellite in view. The primary disadvantage is that in comparison with radar signals, GPS signals are weaker, necessitating larger antennas and/or longer observations. This GPS-reflection remote-sensing system was tested in aircraft and made to record and process both (1) signals coming directly from GPS satellites by means of an upward-looking antenna and (2) GPS signals reflected from the ground by means of a downward-looking antenna. In addition to performing conventional GPS processing, the system records raw signals for postprocessing as required

    Ladar System Identifies Obstacles Partly Hidden by Grass

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    A ladar-based system now undergoing development is intended to enable an autonomous mobile robot in an outdoor environment to avoid moving toward trees, large rocks, and other obstacles that are partly hidden by tall grass. The design of the system incorporates the assumption that the robot is capable of moving through grass and provides for discrimination between grass and obstacles on the basis of geometric properties extracted from ladar readings as described below. The system (see figure) includes a ladar system that projects a range-measuring pulsed laser beam that has a small angular width of radians and is capable of measuring distances of reflective objects from a minimum of dmin to a maximum of dmax. The system is equipped with a rotating mirror that scans the beam through a relatively wide angular range of in a horizontal plane at a suitable small height above the ground. Successive scans are performed at time intervals of seconds. During each scan, the laser beam is fired at relatively small angular intervals of radians to make range measurements, so that the total number of range measurements acquired in a scan is Ne = /

    Obstacle detection for autonomous navigation : an LDRD final report.

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    Traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data

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    Scene perception and traversability analysis are real challenges for autonomous driving systems. In the context of off-road autonomy, there are additional challenges due to the unstructured environments and the existence of various vegetation types. It is necessary for the Autonomous Ground Vehicles (AGVs) to be able to identify obstacles and load-bearing surfaces in the terrain to ensure a safe navigation (McDaniel et al. 2012). The presence of vegetation in off-road autonomy applications presents unique challenges for scene understanding: 1) understory vegetation makes it difficult to detect obstacles or to identify load-bearing surfaces; and 2) trees are usually regarded as obstacles even though only trunks of the trees pose collision risk in navigation. The overarching goal of this dissertation was to study traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data. More specifically, to address the aforementioned challenges, this dissertation studied the impacts of the understory vegetation density on the solid obstacle detection performance of the off-road autonomous systems. By leveraging a physics-based autonomous driving simulator, a classification-based machine learning framework was proposed for obstacle detection based on point cloud data captured by LIDAR. Features were extracted based on a cumulative approach meaning that information related to each feature was updated at each timeframe when new data was collected by LIDAR. It was concluded that the increase in the density of understory vegetation adversely affected the classification performance in correctly detecting solid obstacles. Additionally, a regression-based framework was proposed for estimating the understory vegetation density for safe path planning purposes according to which the traversabilty risk level was regarded as a function of estimated density. Thus, the denser the predicted density of an area, the higher the risk of collision if the AGV traversed through that area. Finally, for the trees in the terrain, the dissertation investigated statistical features that can be used in machine learning algorithms to differentiate trees from solid obstacles in the context of forested off-road scenes. Using the proposed extracted features, the classification algorithm was able to generate high precision results for differentiating trees from solid obstacles. Such differentiation can result in more optimized path planning in off-road applications

    Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

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    Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

    A Nonparametric Approach to Segmentation of Ladar Images

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    The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, namely the ability to discern pulse-spreading effects of elongated targets. A concert of non-parametric statistics and familiar image processing methods are used to calculate the orientation angle of the illuminated objects, and the deficiency of the hardware is circumvented. Segmentation of the various ladar images performed as part of the angle estimation, and this is shown to be a new and effective strategy for analyzing the output of the AFIT ladar camera
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