In the presented scenario, an autonomous surface vehicle (ASV) equipped with a laser scanner navigates on a inland pathway surrounded and crossed by man-made structures such as bridges and locks. GPS receiver present on board experiences signal loss and multipath reflections in situation when the view of the sky is obscured by a bridge or tall buildings. In both cases, a potentially dangerous situation is provoked as the robot has no or inaccurate positioning data. A sensor data processing scheme is proposed where these gaps are smoothly filled in by positioning data generated from scan matching and registration of the laser data. This article shows preliminary results of positioning data improvement during trials in harbor-river environment.
LiDAR , or 3-D laser scanners provide a measurement of distance along an array of laser beams typically mounted on a rotating head, so that they can sweep a large volume of the environment. They are commonly used in autonomous driving @cite_15 but they are also making their way underwater where they enable such applications as autonomous or milimetric-precision survey @cite_14 . 3-D scanners produce a considerable volume of data in form of structures clouds of 3-D points, although some devices can also output depth images. In addition to the geometric information, the intensity of laser reflection is often recorded for every point.
@cite_15 @cite_14
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One of the main applications of the terrestrial laser scanner is the visualization, modeling and monitoring of man-made structures like buildings. Especially surveying applications require on one hand a quickly obtainable, high resolution point cloud but also need observations with a known and well described quality. To obtain a 3D point cloud, the scene is scanned from different positions around the considered object. The scanning geometry plays an important role in the quality of the resulting point cloud. The ideal set-up for scanning a surface of an object is to position the laser scanner in such a way that the laser beam is near perpendicular to the surface. Due to scanning conditions, such an ideal set-up is in practice not possible. The different incidence angles and ranges of the laser beam on the surface result in 3D points of varying quality. The stand-point of the scanner that gives the best accuracy is generally not known. Using an optimal stand-point of the laser scanner on a scene will improve the quality of individual point measurements and results in a more uniform registered point cloud. The design of an optimum measurement setup is defined such that the optimum stand-points are identified to fulfill predefined quality requirements and to ensure a complete spatial coverage. The additional incidence angle and range constraints on the visibility from a view point ensure that individual scans are not affected by bad scanning geometry effects. A complex and large room that would normally require five view point to be fully covered, would require nineteen view points to obtain full coverage under the range and incidence angle constraints. Portable laser range-finders, further referred to as LIDAR, and simultaneous localization and mapping (SLAM) are an efficient method of acquiring as-built floor plans. Generating and visualizing floor plans in real-time helps the operator assess the quality and coverage of capture data. Building a portable capture platform necessitates operating under limited computational resources. We present the approach used in our backpack mapping platform which achieves real-time mapping and loop closure at a 5 cm resolution. To achieve realtime loop closure, we use a branch-and-bound approach for computing scan-to-submap matches as constraints. We provide experimental results and comparisons to other well known approaches which show that, in terms of quality, our approach is competitive with established techniques. 3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9 ) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness blind spots of the neural network can be fixed. Advances in autonomous inspection of subsea facilities using a 3 Dimensional(3D) LIght Detection And Ranging (LiDAR) sensor are examined to illustrate the favorable enhancement of safety, reliability, reduction in risks, economic benefits and superior data products compared to conventional means. These benefits provide operators with significant improvements over general visual inspection by the addition of sensors that produce 3D models of the structure being inspected. Examples are provided illustrating test data from operations conducted from 2012-2013. Supported by funding from the Research Partnership to Secure Energy for America (RPSEA) Lockheed Martin MST teamed with the innovative company 3D at Depth to incorporate their new DP2TM 3D LiDAR onto the proven Marlin® Autonomous Underwater Vehicle. The objectives of the project include: Survey using combination of high resolution 3D Sonar and 3D LiDAR Generation of high fidelity 3D models Detection and localization of structural changes vs. reference model Lockheed Martin has successfully integrated the laser into the Marlin shore based laboratory addressing the challenges associated with coordinating a scanning laser from a moving platform and producing high resolution georegistered images. Testing will be conducted in early 2014 to validate the system in the Atlantic Ocean waters offshore Palm Beach, Florida. Three dimensional georegistered models of an entire scene can be rapidly collected providing a clear vision of the underwater scene along high resolution 3D models of the imaged item. Data collected at sea and in the laboratory will be presented demonstrating the performance of the system. Application to deepwater life of field inspection will be presented with evidence gained from offshore trials. This emergent technology supports Subsea Facility Inspection Repair and Maintenance, Integrity Management Inspections of Marine Risers, Moorings and anchors, Subsea Pipelines, Flowlines, Umbilicals, and supporting subsea infrastructure.