A Drift-Resilient and Degeneracy-Aware Loop Closure Detection Method for Localization and Mapping In Perceptually-Degraded Environments

Enabling fully autonomous robots capable of navigating and exploring unknown and complex environments has been at the core of robotics research for several decades. Mobile robots rely on a model of the environment for functions like manipulation, collision avoidance and path planning. In GPS-denied and unknown environments where a prior map of the environment is not available, robots need to rely on the onboard sensing to obtain locally accurate maps to operate in their local environment. A global map of an unknown environment can be constructed from fusion of local maps of temporally or spatially distributed mobile robots in the environment. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop closure detection enables finding the correspondences between the local maps obtained by individual robots and merging them into a consistent global map of the environment. In ambiguous and perceptually-degraded environments, robust detection of intra- and inter-robot loop closures is especially challenging. This is due to poor illumination or lack-thereof, self-similarity, and sparsity of distinctive perceptual landmarks and features sufficient for establishing global position. Overcoming these challenges enables a wide range of terrestrial and planetary applications, ranging from search and rescue, and disaster relief in hostile environments, to robotic exploration of lunar and Martian surfaces, caves and lava tubes that are of particular interest as they can provide potential habitats for future manned space missions. In this dissertation, methods and metrics are developed for resolving location ambiguities to significantly improve loop closures in perceptually-degraded environments with sparse or undifferentiated features. The first contribution of this dissertation is development of a degeneracy-aware SLAM front-end capable of determining the level of geometric degeneracy in an unknown environment based on computing the Hessian associated with the computed optimal transformation from lidar scan matching. Using this crucial capability, featureless areas that could lead to data association ambiguity and spurious loop closures are determined and excluded from the search for loop closures. This significantly improves the quality and accuracy of localization and mapping, because the search space for loop closures can be expanded as needed to account for drift while decreasing rather than increasing the probability of false loop closure detections. The second contribution of this dissertation is development of a drift-resilient loop closure detection method that relies on the 2D semantic and 3D geometric features extracted from lidar point cloud data to enable detection of loop closures with increased robustness and accuracy as compared to traditional geometric methods. The proposed method achieves higher performance by exploiting the spatial configuration of the local scenes embedded in 2D occupancy grid maps commonly used in robot navigation, to search for putative loop closures in a pre-matching step before using a geometric verification. The third contribution of this dissertation is an extensive evaluation and analysis of performance and comparison with the state-of-the-art methods in simulation and in real-world, including six challenging underground mines across the United States

Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of City Models

This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable

GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics

Anomaly detection is the process of finding data points that deviate from a baseline. In a real-life setting, anomalies are usually unknown or extremely rare. Moreover, the detection must be accomplished in a timely manner or the risk of corrupting the system might grow exponentially. In this work, we propose a two level framework for detecting anomalies in sequences of discrete elements. First, we assess whether we can obtain enough information from the statistics collected from the discriminator's layers to discriminate between out of distribution and in distribution samples. We then build an unsupervised anomaly detection module based on these statistics. As to augment the data and keep track of classes of known data, we lean toward a semi-supervised adversarial learning applied to discrete elements.Comment: 5 pages, 53rd Annual Conference on Information Sciences and Systems, CISS 201

Towards extracting 3-D structural representations of AGR core fuel channels from 2-D in-core inspection videos

Remote Visual Inspection (RVI) of Advanced Gas-cooled Reactor (AGR) nuclear power stations allows engineers to gain an understanding of the AGR graphite core health by investigating the incorporated fuel channels. During planned, periodic outages, video footage of the pre-selected fuel channels is acquired using specialist inspection tools and is subsequently taken offline for further analysis using visualization techniques. Current methods of visualization however provide limited structural information due to the loss of depth information as a direct result of the image acquisition process. This paper introduces a new bespoke 3-D reconstruction framework to recover lost depth information to produce 3-D point cloud reconstructions of fuel channels from inspection videos. We also present here a new, lab based, experimental rig setup with which we effectively captured data under lab controlled conditions to verify our 3-D reconstruction algorithms. Our proposed method is tested on 2-D in core inspection videos in addition to the footage captured within laboratory conditions and outperforms state-of-the-art incremental reconstruction frameworks whilst producing a more representative 3-D point cloud for improved in-core visualization