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

Topological segmentation of indoors/outdoors sequences of spherical views

International audienceTopological navigation consists for a robot in navigating in a topological graph which nodes are topological places. Either for indoor or outdoor environments, segmen- tation into topological places is a challenging issue. In this paper, we propose a common approach for indoor and out- door environment segmentation without elaborating a complete topological navigation system. The approach is novel in that environment sensing is performed using spherical images. Envi- ronment structure estimation is performed by a global structure descriptor specially adapted to the spherical representation. This descriptor is processed by a custom designed algorithm which detects change-points defining the segmentation between topological places

A dataset of annotated omnidirectional videos for distancing applications

Omnidirectional (or 360◦ ) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360◦ videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360◦ image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Visual Indoor Localization in Known Environments

In this letter, we propose a visual indoor localization technique, which localizes a camera sensor by comparing the acquired images to a reference model of location-tagged visual features. The proposed method relies on an efficient way to search for feature matches, which can run in real time. Experimental results show good localization accuracy even in challenging scenarios

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Holoscopic 3D imaging and display technology: Camera/ processing/ display

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHoloscopic 3D imaging “Integral imaging” was first proposed by Lippmann in 1908. It has become an attractive technique for creating full colour 3D scene that exists in space. It promotes a single camera aperture for recording spatial information of a real scene and it uses a regularly spaced microlens arrays to simulate the principle of Fly’s eye technique, which creates physical duplicates of light field “true 3D-imaging technique”. While stereoscopic and multiview 3D imaging systems which simulate human eye technique are widely available in the commercial market, holoscopic 3D imaging technology is still in the research phase. The aim of this research is to investigate spatial resolution of holoscopic 3D imaging and display technology, which includes holoscopic 3D camera, processing and display. Smart microlens array architecture is proposed that doubles spatial resolution of holoscopic 3D camera horizontally by trading horizontal and vertical resolutions. In particular, it overcomes unbalanced pixel aspect ratio of unidirectional holoscopic 3D images. In addition, omnidirectional holoscopic 3D computer graphics rendering techniques are proposed that simplify the rendering complexity and facilitate holoscopic 3D content generation. Holoscopic 3D image stitching algorithm is proposed that widens overall viewing angle of holoscopic 3D camera aperture and pre-processing of holoscopic 3D image filters are proposed for spatial data alignment and 3D image data processing. In addition, Dynamic hyperlinker tool is developed that offers interactive holoscopic 3D video content search-ability and browse-ability. Novel pixel mapping techniques are proposed that improves spatial resolution and visual definition in space. For instance, 4D-DSPM enhances 3D pixels per inch from 44 3D-PPIs to 176 3D-PPIs horizontally and achieves spatial resolution of 1365 × 384 3D-Pixels whereas the traditional spatial resolution is 341 × 1536 3D-Pixels. In addition distributed pixel mapping is proposed that improves quality of holoscopic 3D scene in space by creating RGB-colour channel elemental images

A multisensor SLAM for dense maps of large scale environments under poor lighting conditions

This thesis describes the development and implementation of a multisensor large scale autonomous mapping system for surveying tasks in underground mines. The hazardous nature of the underground mining industry has resulted in a push towards autonomous solutions to the most dangerous operations, including surveying tasks. Many existing autonomous mapping techniques rely on approaches to the Simultaneous Localization and Mapping (SLAM) problem which are not suited to the extreme characteristics of active underground mining environments. Our proposed multisensor system has been designed from the outset to address the unique challenges associated with underground SLAM. The robustness, self-containment and portability of the system maximize the potential applications.The multisensor mapping solution proposed as a result of this work is based on a fusion of omnidirectional bearing-only vision-based localization and 3D laser point cloud registration. By combining these two SLAM techniques it is possible to achieve some of the advantages of both approaches – the real-time attributes of vision-based SLAM and the dense, high precision maps obtained through 3D lasers. The result is a viable autonomous mapping solution suitable for application in challenging underground mining environments.A further improvement to the robustness of the proposed multisensor SLAM system is a consequence of incorporating colour information into vision-based localization. Underground mining environments are often dominated by dynamic sources of illumination which can cause inconsistent feature motion during localization. Colour information is utilized to identify and remove features resulting from illumination artefacts and to improve the monochrome based feature matching between frames.Finally, the proposed multisensor mapping system is implemented and evaluated in both above ground and underground scenarios. The resulting large scale maps contained a maximum offset error of ±30mm for mapping tasks with lengths over 100m