A Compact Spherical RGBD Keyframe-based Representation

International audience— This paper proposes an environmental representation approach based on hybrid metric and topological maps as a key component for mobile robot navigation. Focus is made on an ego-centric pose graph structure by the use of Keyframes to capture the local properties of the scene. With the aim of reducing data redundancy, suppress sensor noise whilst maintaining a dense compact representation of the environment, neighbouring augmented spheres are fused in a single representation. To this end, an uncertainty error model propagation is formulated for outlier rejection and data fusion, enhanced with the notion of landmark stability over time. Finally, our algorithm is tested thoroughly on a newly developed wide angle 360 0 field of view (FOV) spherical sensor where improvements such as trajectory drift, compactness and reduced tracking error are demonstrated

NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM

Neural field-based 3D representations have recently been adopted in many areas including SLAM systems. Current neural SLAM or online mapping systems lead to impressive results in the presence of simple captures, but they rely on a world-centric map representation as only a single neural field model is used. To define such a world-centric representation, accurate and static prior information about the scene, such as its boundaries and initial camera poses, are required. However, in real-time and on-the-fly scene capture applications, this prior knowledge cannot be assumed as fixed or static, since it dynamically changes and it is subject to significant updates based on run-time observations. Particularly in the context of large-scale mapping, significant camera pose drift is inevitable, necessitating the correction via loop closure. To overcome this limitation, we propose NEWTON, a view-centric mapping method that dynamically constructs neural fields based on run-time observation. In contrast to prior works, our method enables camera pose updates using loop closures and scene boundary updates by representing the scene with multiple neural fields, where each is defined in a local coordinate system of a selected keyframe. The experimental results demonstrate the superior performance of our method over existing world-centric neural field-based SLAM systems, in particular for large-scale scenes subject to camera pose updates

Cartographie dense basée sur une représentation compacte RGB-D dédiée à la navigation autonome

Our aim is concentrated around building ego-centric topometric maps represented as a graph of keyframe nodes which can be efficiently used by autonomous agents. The keyframe nodes which combines a spherical image and a depth map (augmented visual sphere) synthesises information collected in a local area of space by an embedded acquisition system. The representation of the global environment consists of a collection of augmented visual spheres that provide the necessary coverage of an operational area. A "pose" graph that links these spheres together in six degrees of freedom, also defines the domain potentially exploitable for navigation tasks in real time. As part of this research, an approach to map-based representation has been proposed by considering the following issues : how to robustly apply visual odometry by making the most of both photometric and ; geometric information available from our augmented spherical database ; how to determine the quantity and optimal placement of these augmented spheres to cover an environment completely ; how tomodel sensor uncertainties and update the dense infomation of the augmented spheres ; how to compactly represent the information contained in the augmented sphere to ensure robustness, accuracy and stability along an explored trajectory by making use of saliency maps.Dans ce travail, nous proposons une représentation efficace de l’environnement adaptée à la problématique de la navigation autonome. Cette représentation topométrique est constituée d’un graphe de sphères de vision augmentées d’informations de profondeur. Localement la sphère de vision augmentée constitue une représentation égocentrée complète de l’environnement proche. Le graphe de sphères permet de couvrir un environnement de grande taille et d’en assurer la représentation. Les "poses" à 6 degrés de liberté calculées entre sphères sont facilement exploitables par des tâches de navigation en temps réel. Dans cette thèse, les problématiques suivantes ont été considérées : Comment intégrer des informations géométriques et photométriques dans une approche d’odométrie visuelle robuste ; comment déterminer le nombre et le placement des sphères augmentées pour représenter un environnement de façon complète ; comment modéliser les incertitudes pour fusionner les observations dans le but d’augmenter la précision de la représentation ; comment utiliser des cartes de saillances pour augmenter la précision et la stabilité du processus d’odométrie visuelle

A dense map building approach from spherical RGBD images

International audienceVisual mapping is a required capability for practical autonomous mobile robots where there exists a grow- ing industry with applications ranging from the service to industrial sectors. Prior to map building, Visual Odometry(VO) is an essential step required in the process of pose graph construction. In this work, we first propose to tackle the pose estimation problem by using both photometric and geometric information in a direct RGBD image registration method. Secondly, the mapping problem is tackled with a pose graph representation, whereby, given a database of augmented visual spheres, a travelled trajectory with redundant information is pruned out to a skeletal pose graph. Both methods are evaluated with data acquired with a recently proposed omnidirectional RGBD sensor for indoor environments

Dynamic HDR Environment Capture for Mixed Reality

Rendering accurate and convincing virtual content into mixed reality (MR) scenes requires detailed illumination information about the real environment. In existing MR systems, this information is often captured using light probes [1, 8, 9, 17, 19--21], or by reconstructing the real environment as a preprocess [31, 38, 54]. We present a method for capturing and updating a HDR radiance map of the real environment and tracking camera motion in real time using a self-contained camera system, without prior knowledge about the real scene. The method is capable of producing plausible results immediately and improving in quality as more of the scene is reconstructed. We demonstrate how this can be used to render convincing virtual objects whose illumination changes dynamically to reflect the changing real environment around them

Increasing the Convergence Domain of RGB-D Direct Registration Methods for Vision-based Localization in Large Scale Environments

International audienceDeveloping autonomous vehicles capable of dealing with complex and dynamic unstructured environments over large-scale distances, remains a challenging goal. One of the major difficulties in this objective is the precise localization of the vehicle within its environment so that autonomous navigation techniques can be employed. In this context, this paper presents a methodology to map building and to efficient pose computation which is specially adapted for cases of large displacements. Our method uses hybrid robust RGB-D cost functions that have different convergence properties, whilst exploiting the visibility rotation invariance given by panoramic spherical images. The proposed registration model is composed of a RGB and point-to-plane ICP cost in a multi-resolution framework. We close up the paper presenting mapping and localization results in real outdoor scenes

HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling

Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel -- a novel 6-DoF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-DoF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUDA code.Comment: Project page: https://hyperreel.github.io

A Novel Method for Extrinsic Calibration of Multiple RGB-D Cameras Using Descriptor-Based Patterns

This letter presents a novel method to estimate the relative poses between RGB-D cameras with minimal overlapping fields of view in a panoramic RGB-D camera system. This calibration problem is relevant to applications such as indoor 3D mapping and robot navigation that can benefit from a 360$^\circ$ field of view using RGB-D cameras. The proposed approach relies on descriptor-based patterns to provide well-matched 2D keypoints in the case of a minimal overlapping field of view between cameras. Integrating the matched 2D keypoints with corresponding depth values, a set of 3D matched keypoints are constructed to calibrate multiple RGB-D cameras. Experiments validated the accuracy and efficiency of the proposed calibration approach, both superior to those of existing methods (800 ms vs. 5 seconds; rotation error of 0.56 degrees vs. 1.6 degrees; and translation error of 1.80 cm vs. 2.5 cm.Comment: 6 pages, 7 figures, under review by IEEE Robotics and Automation Letters & ICR

A Survey on Global LiDAR Localization

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. In the last two decades, LiDAR scanners have become a standard sensor for robot localization and mapping. This article surveys recent progress and advances in LiDAR-based global localization. We start with the problem formulation and explore the application scope. We then present the methodology review covering various global localization topics, such as maps, descriptor extraction, and consistency checks. The contents are organized under three themes. The first is the combination of global place retrieval and local pose estimation. Then the second theme is upgrading single-shot measurement to sequential ones for sequential global localization. The third theme is extending single-robot global localization to cross-robot localization on multi-robot systems. We end this survey with a discussion of open challenges and promising directions on global lidar localization