Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

Graph-Based Classification of Omnidirectional Images

Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods are designed for planar images that have different geometrical properties than omnidirectional ones. In this paper we study image classification task by taking into account the specific geometry of omnidirectional cameras with graph-based representations. In particular, we extend deep learning architectures to data on graphs; we propose a principled way of graph construction such that convolutional filters respond similarly for the same pattern on different positions of the image regardless of lens distortions. Our experiments show that the proposed method outperforms current techniques for the omnidirectional image classification problem

Towards Visual Ego-motion Learning in Robots

Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped ego-motion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures, 2 table

A vision system for mobile maritime surveillance platforms

Mobile surveillance systems play an important role to minimise security and safety threats in high-risk or hazardous environments. Providing a mobile marine surveillance platform with situational awareness of its environment is important for mission success. An essential part of situational awareness is the ability to detect and subsequently track potential target objects.Typically, the exact type of target objects is unknown, hence detection is addressed as a problem of finding parts of an image that stand out in relation to their surrounding regions or are atypical to the domain. Contrary to existing saliency methods, this thesis proposes the use of a domain specific visual attention approach for detecting potential regions of interest in maritime imagery. For this, low-level features that are indicative of maritime targets are identified. These features are then evaluated with respect to their local, regional, and global significance. Together with a domain specific background segmentation technique, the features are combined in a Bayesian classifier to direct visual attention to potential target objects.The maritime environment introduces challenges to the camera system: gusts, wind, swell, or waves can cause the platform to move drastically and unpredictably. Pan-tilt-zoom cameras that are often utilised for surveillance tasks can adjusting their orientation to provide a stable view onto the target. However, in rough maritime environments this requires high-speed and precise inputs. In contrast, omnidirectional cameras provide a full spherical view, which allows the acquisition and tracking of multiple targets at the same time. However, the target itself only occupies a small fraction of the overall view. This thesis proposes a novel, target-centric approach for image stabilisation. A virtual camera is extracted from the omnidirectional view for each target and is adjusted based on the measurements of an inertial measurement unit and an image feature tracker. The combination of these two techniques in a probabilistic framework allows for stabilisation of rotational and translational ego-motion. Furthermore, it has the specific advantage of being robust to loosely calibrated and synchronised hardware since the fusion of tracking and stabilisation means that tracking uncertainty can be used to compensate for errors in calibration and synchronisation. This then completely eliminates the need for tedious calibration phases and the adverse effects of assembly slippage over time.Finally, this thesis combines the visual attention and omnidirectional stabilisation frameworks and proposes a multi view tracking system that is capable of detecting potential target objects in the maritime domain. Although the visual attention framework performed well on the benchmark datasets, the evaluation on real-world maritime imagery produced a high number of false positives. An investigation reveals that the problem is that benchmark data sets are unconsciously being influenced by human shot selection, which greatly simplifies the problem of visual attention. Despite the number of false positives, the tracking approach itself is robust even if a high number of false positives are tracked

Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning

Autonomous agents in any environment require accurate and reliable position and motion estimation to complete their required tasks. Many different sensor modalities have been utilized for this task such as GPS, ultra-wide band, visual simultaneous localization and mapping (SLAM), and light detection and ranging (LiDAR) SLAM. Many of the traditional positioning systems do not take advantage of the recent advances in the machine learning field. In this work, an omnidirectional camera position estimation system relying primarily on a learned model is presented. The positioning system benefits from the wide field of view provided by an omnidirectional camera. Recent developments in the self-supervised learning field for generating useful features from unlabeled data are also assessed. A novel radial patch pretext task for omnidirectional images is presented in this work. The resulting implementation will be a robot localization and tracking algorithm that can be adapted to a variety of environments such as warehouses and college campuses. Further experiments with additional types of sensors including 3D LiDAR, 60 GHz wireless, and Ultra-Wideband localization systems utilizing machine learning are also explored. A fused learned localization model utilizing multiple sensor modalities is evaluated in comparison to individual sensor models