Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Active Object Classification from 3D Range Data with Mobile Robots

This thesis addresses the problem of how to improve the acquisition of 3D range data with a mobile robot for the task of object classification. Establishing the identities of objects in unknown environments is fundamental for robotic systems and helps enable many abilities such as grasping, manipulation, or semantic mapping. Objects are recognised by data obtained from sensor observations, however, data is highly dependent on viewpoint; the variation in position and orientation of the sensor relative to an object can result in large variation in the perception quality. Additionally, cluttered environments present a further challenge because key data may be missing. These issues are not always solved by traditional passive systems where data are collected from a fixed navigation process then fed into a perception pipeline. This thesis considers an active approach to data collection by deciding where is most appropriate to make observations for the perception task. The core contributions of this thesis are a non-myopic planning strategy to collect data efficiently under resource constraints, and supporting viewpoint prediction and evaluation methods for object classification. Our approach to planning uses Monte Carlo methods coupled with a classifier based on non-parametric Bayesian regression. We present a novel anytime and non-myopic planning algorithm, Monte Carlo active perception, that extends Monte Carlo tree search to partially observable environments and the active perception problem. This is combined with a particle-based estimation process and a learned observation likelihood model that uses Gaussian process regression. To support planning, we present 3D point cloud prediction algorithms and utility functions that measure the quality of viewpoints by their discriminatory ability and effectiveness under occlusion. The utility of viewpoints is quantified by information-theoretic metrics, such as mutual information, and an alternative utility function that exploits learned data is developed for special cases. The algorithms in this thesis are demonstrated in a variety of scenarios. We extensively test our online planning and classification methods in simulation as well as with indoor and outdoor datasets. Furthermore, we perform hardware experiments with different mobile platforms equipped with different types of sensors. Most significantly, our hardware experiments with an outdoor robot are to our knowledge the first demonstrations of online active perception in a real outdoor environment. Active perception has broad significance in many applications. This thesis emphasises the advantages of an active approach to object classification and presents its assimilation with a wide range of robotic systems, sensors, and perception algorithms. By demonstration of performance enhancements and diversity, our hope is that the concept of considering perception and planning in an integrated manner will be of benefit in improving current systems that rely on passive data collection

Analysis of the inspection of mechanical parts using dense range data

More than ever, efficiency and quality are key words in modern industry. This situation enhances the importance of quality control and creates a great demand for cheap and reliable automatic inspection systems. Taking into account these facts and the demand for systems able to inspect the final shape of machined parts, we decided to investigate the viability of automatic model-based inspection of mechanical parts using the dense range data produced by laser stripers. Given a part to be inspected and a corresponding model of the part stored in the model data base, the first step of inspecting the part is the acquisition of data corresponding to the part, in our case this means the acquisition of a range image of it. In order to be able to compare the part image and its stored model, it is necessary to align the model with the range image of the part. This process, called registration, corresponds to finding the rigid transformation that superposes model and image. After the image and model are registered, the actual inspection uses the range image to verify if all the features predicted in the model are present and have the right pose and dimensions. Therefore, besides the acquisition of range images, the inspection of machined parts involves three main issues: modelling, registration and inspection diagnosis. The application, for inspection purposes, of the main representational schemes for modelling solid objects is discussed and it is suggested the use of EDT models (see [Zeid 91]). A particular implementation of EDT models is presented. A novel approach for the verification of tolerances during the inspection is proposed. The approach allows not only the inspection of the most common tolerances described in the tolerancing standards, but also the inspection of tolerances defined according to Requicha's theory of tolerancing (see [Requicha 83]). A model of the sensitivity and reliability of the inspection process based on the modelling of the errors during the inspection process is also proposed. The importance of the accuracy of the registration in different inspections tasks is discussed. A modified version of the ICP algorithm (see [Besl &; McKay 92]) for the registration of sculptured surfaces is proposed. The maximum accuracy of the ICP algorithm, as a function of the sensor errors and the number of matched points, is determined. A novel method for the measurement and reconstruction of waviness errors on sculp¬ tured surfaces is proposed. The method makes use of the 2D Discrete Fourier Transform for the detection and reconstruction of the waviness error. A model of the sensitivity and reliability of the method is proposed. The application of the methods proposed is illustrated using synthetic and real range image

Extrinsic Calibration and Ego-Motion Estimation for Mobile Multi-Sensor Systems

Autonomous robots and vehicles are often equipped with multiple sensors to perform vital tasks such as localization or mapping. The joint system of various sensors with different sensing modalities can often provide better localization or mapping results than individual sensor alone in terms of accuracy or completeness. However, to enable improved performance, two important challenges have to be addressed when dealing with multi-sensor systems. Firstly, how to accurately determine the spatial relationship between individual sensor on the robot? This is a vital task known as extrinsic calibration. Without this calibration information, measurements from different sensors cannot be fused. Secondly, how to combine data from multiple sensors to correct for the deficiencies of each sensor, and thus, provides better estimations? This is another important task known as data fusion. The core of this thesis is to provide answers to these two questions. We cover, in the first part of the thesis, aspects related to improving the extrinsic calibration accuracy, and present, in the second part, novel data fusion algorithms designed to address the ego-motion estimation problem using data from a laser scanner and a monocular camera. In the extrinsic calibration part, we contribute by revealing and quantifying the relative calibration accuracies of three common types of calibration methods, so as to offer an insight into choosing the best calibration method when multiple options are available. Following that, we propose an optimization approach for solving common motion-based calibration problems. By exploiting the Gauss-Helmert model, our approach is more accurate and robust than classical least squares model. In the data fusion part, we focus on camera-laser data fusion and contribute with two new ego-motion estimation algorithms that combine complementary information from a laser scanner and a monocular camera. The first algorithm utilizes camera image information to guide the laser scan-matching. It can provide accurate motion estimates and yet can work in general conditions without requiring a field-of-view overlap between the camera and laser scanner, nor an initial guess of the motion parameters. The second algorithm combines the camera and the laser scanner information in a direct way, assuming the field-of-view overlap between the sensors is substantial. By maximizing the information usage of both the sparse laser point cloud and the dense image, the second algorithm is able to achieve state-of-the-art estimation accuracy. Experimental results confirm that both algorithms offer excellent alternatives to state-of-the-art camera-laser ego-motion estimation algorithms

Partitioned by Process: Measuring Post-Fire Debris Flow and Rill Erosion with Structure from Motion

In mountainous regions burned by wildfires, profound changes in soil characteristics and combustion of vegetation increase hillslope and channel erosion during storm events. Reduced infiltration and abundant loose sediment produce large post-fire erosional events which endanger human lives and infrastructure and contribute significantly to long-term erosion rates. While the influence of fire in increasing erosion has long been recognized, quantifying volumes and sources of eroded material from burned landscapes is difficult. Pre-erosion high-resolution topographic data (e.g. lidar) are often not available in burned areas and determining specific contributions from post-fire hillslope and channel erosion is challenging. Multiple erosional processes mobilize sediment from hillslopes, but the connectivity of hillslopes to channels controls the basin-wide erosional response. We quantify an important spatial threshold separating hillslope and channel erosion processes in a catchment burned in the 2016 Pioneer Fire. Further, we confirm the impact of post-fire erosion on landscape evolution, demonstrate the applicability of Structure from Motion photogrammetry (SfM) to quantify post-fire erosion without detailed pre-erosion topography, and improve estimates of rill erosion at adequate spatial scales. In this rugged 0.95 km2 watershed in the weathered Idaho Batholith, widespread rilling and channel erosion produced a runoff-generated debris flow following modest precipitation in October 2016. We implemented unmanned aerial vehicle (UAV)-based SfM to derive 5 cm resolution topography of the channel scoured by debris flow. Lacking cm-resolution pre-erosion topography, we created a synthetic surface defined by the debris flow scour’s geomorphic signature and used a DEM of difference (DoD) to map and quantify channel erosion, finding 3467 ± 422 m3 was eroded by debris flow scour. Rill dimensions along hillslope transects and Monte Carlo simulation show rilling eroded ~1100 m3 of sediment and define a volume uncertainty of 29%. Next, we delineated sub-basins within the larger study catchment to investigate the evolution of hillslope and channel erosion with varying contributing areas. We document that a drainage area of 20 ha (0.2 km2) represents the threshold from dominantly hillslope to dominantly channel erosion in this setting. Hillslopes contribute less to total erosion as drainage area increases, reflecting increased connectivity and efficiency of channel networks. Our experimental sub-basin results show a positive relationship between sediment yield (mass/area/time) and drainage area; contrary to most literature. The modern deposit volume was 5700 ± 1140 m3, indicating ~60% contribution from post-fire channel erosion. Our measured total eroded volume (4600 ± 740 m3) aligns closely with the preliminary assessment from the US Geological Survey (USGS) post-fire hazard model for similar, modest precipitation intensities. Holocene alluvial stratigraphic sequences exposed by the 2016 debris flows show fire-related deposition dominates the stratigraphic record. Dating of charcoal fragments preserved in stratigraphy at the catchment outlet and reconstructions of prior deposit volumes provide a record of Holocene fire-related debris flows at this site. Comparisons of fire-related sediment yields from episodic events with Holocene sediment yields reconstructed from other studies in the region suggest episodic wildfire-driven erosion dominates millennial-scale erosion. Further investigations into spatial thresholds of post-fire erosion, hillslope-channel connectivity, and long-term landscape changes, especially when coupled with high resolution topography, will help to quantify the impacts of wildfire in other settings