We present a method for planar shape detection and regularization from raw point sets. The geometric modelling and processing of man-made environments from measurement data often relies upon robust detection of planar primitive shapes. In addition, the detection and reinforcement of regularities between planar parts is a means to increase resilience to missing or defect-laden data as well as to reduce the complexity of models and algorithms down the modelling pipeline. The main novelty behind our method is to perform detection and regularization in tandem. We first sample a sparse set of seeds uniformly on the input point set, and then perform in parallel shape detection through region growing, interleaved with regularization through detection and reinforcement of regular relationships coplanar, parallel and orthogonal. In addition to addressing the end goal of regularization, such reinforcement also improves data fitting and provides guidance for clustering small parts into larger planar parts. We evaluate our approach against a wide range of inputs and under four criteria: geometric fidelity, coverage, regularity and running times. Our approach compares well with available implementations such as the efficient random sample consensus-based approach proposed by Schnabel and co-authors in 2007.

/pdf/planar-shape-detection-and-regularization-in-tandem-3fgesd4s22.pdf

Planar Shape Detection and Regularization in Tandem

The integration of several range cameras in a mobile platform is useful for applications in mobile robotics and autonomous vehicles that require a large field of view. This situation is increasingly interesting with the advent of low cost range cameras like those developed by Primesense. Calibrating such combination of sensors for any geometric configuration is a problem that has been recently solved through visual odometry (VO) and SLAM. However, this kind of solution is laborious to apply, requiring robust SLAM or VO in controlled environments. In this paper we propose a new uncomplicated technique for extrinsic calibration of range cameras that relies on finding and matching planes. The method that we present serves to calibrate two or more range cameras in an arbitrary configuration, requiring only to observe one plane from differ- ent viewpoints. The conditions to solve the problem are studied, and several practical examples are presented covering different geometric configurations, including an omnidirectional RGB- D sensor composed of 8 range cameras. The quality of this calibration is evaluated with several experiments that demon- strate an improvement of accuracy over design parameters, while providing a versatile solution that is extremely fast and easy to apply.

/pdf/extrinsic-calibration-of-a-set-of-range-cameras-in-5-seconds-10demm0zlk.pdf

Extrinsic calibration of a set of range cameras in 5 seconds without pattern

When I started work on beads, the first task was to register over a thousand strings of them in the Petrie Collection. At that time, I did not know what I might need in the later stage of my work, let alone what other people in the field or museum my require for their purposes. The obvious course naturally resorted to is to follow some system or other with certain modifications. As stated by Beck, “to describe a bead fully it is necessary, to state its form, perforation, colour, material, and decoration” But for the registration of whole strings of beads, there are some other informations which should be recorded as well.

Method of Registration

https://hal.inria.fr/hal-01237845/document

Scene structure registration for localization and mapping

An obstacle in old document interpretation comes from the lack of image quality. Old documents frequently appear with digitization errors, uneven background, bleed-through effect. A new approach based on morphological color operators to restore the color text is presented. The morphological tools are based on three color spaces, HSI well known in morphological processes, YCrCb and YIQ rarely used in morphological procedures. Experimental results carried onto 100 old documents have proven that using YCrCb and YIQ is as effective as using HSI to recover ancient texts in uneven and foxed background images, without presenting problems in hue ordination.

Restoration Of Old Document Images Using Different Color Spaces: Restoration Of Old Document Images

Visual mapping is a required capability for practical autonomous mobile robots where there exists a growing 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.

https://hal.archives-ouvertes.fr/hal-01086902/document

A dense map building approach from spherical RGBD images

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° field of view (FOV) spherical sensor where improvements such as trajectory drift, compactness and reduced tracking error are demonstrated.

/pdf/a-compact-spherical-rgbd-keyframe-based-representation-2zr6i8mxpp.pdf

A compact spherical RGBD keyframe-based representation

Nous presentons une nouvelle representation hybride metrique/topologique/semantique appelee MTS-Maps concue pour la conduite automatique de vehicule autonome dans des environnements de grande taille d'interieur ou d'exterieur. Basee sur une representation generique, la sphere RGB-DL, elle permet de s'affranchir de la technologie du capteur utilise. Ces differentes couches permettent de traiter le probleme de localisation allant de la requete contextuelle jusqu'au calcul de l'erreur de pose utilise dans le feedback du controleur. Les resultats presentes montrent ses performances a la fois au niveau metrique par une experimentation de conduite automatique dans un environnement urbain et au niveau semantique par une comparaison avec une implementation recente d'une methode Bag-of-Words.

https://hal.archives-ouvertes.fr/hal-00988843/document

Tawsif Gokhool

Papers

A dense map building approach from spherical RGBD images

A compact spherical RGBD keyframe-based representation

Représentation orientée navigation d'environnements à grande échelle