nils-herrmann/clean_scirepeval huggingface dataset:
Homography Based Egomotion Estimation with a Common Direction

In this paper, we explore the different minimal solutions for egomotion estimation of a camera based on homography knowing the gravity vector between calibrated images. These solutions depend on the prior knowledge about the reference plane used by the homography. We then demonstrate that the number of matched points can vary from two to three and that a direct closed-form solution or a Gröbner basis based solution can be derived according to this plane. Many experimental results on synthetic and real sequences in indoor and outdoor environments show the efficiency and the robustness of our approach compared to standard methods.
Vision based attitude and altitude estimation for UAVs in dark environments

This paper presents a system dedicated to the real-time estimation of attitude and altitude for unmanned aerial vehicles (UAV) under low light and dark environment. This system consists in a fisheye camera, which allows to cover a large field of view (FOV), and a laser circle projector mounted on a fixed baseline. The approach, close to structured light systems, uses the geometrical information obtained by the projection of the laser circle onto the ground plane and perceived by the camera. We present a theoretical study of the system in which the camera is modelled as a sphere and show that the estimation of a conic on this sphere allows to obtain the attitude and the altitude of the robot. We propose some experiments based on simulated data and real sequences. The estimated attitude and altitude from our method are comparable with commercial sensors in terms of its accuracy and correctness. The results also prove its suitability for autonomous take-off and landing as well as for the case of low altitude manoeuvre in dark environments. It also provides room for additional payload to be used for different applications due to use of light weight micro-camera and laser system.
Incremental map building for mobile robot navigation in an indoor environment

In this article we present a navigation system allowing a mobile robot to be localized in an indoor environment which is only partially known. This system integrates an environment map updating module allowing the mobile robot to estimate the position of new vertical landmarks along its path.  The integration of new landmarks into the environment global map is managed from the covariance matrix associated with each unlisted landmark. We present the experimental results we have got with SARAH, our mobile robot.
Wide-baseline omni-stereo at junctions: Extrinsic auto-calibration, trajectory and speed estimation

In recent years, there has been an increasing interest for vision-based monitoring of intersections. Most of the works use perspective cameras that work independently. In this paper, we present a wide-baseline stereoscopic system composed of fisheye cameras, perfectly suitable for rural or unsignalized intersections. Accurate extrinsic calibration is required to compute metric information. But the task is quite challenging, because of the wide-baseline and the vegetation. Also, pattern-based methods are hardly feasible without disrupting the traffic. Therefore, we propose a points-correspondence-free solution. Our method is fully-automatic and based on a joint analysis of vehicles motion and appearance. Provided the extrinsic calibration estimated, we achieve trajectory reconstruction with a Kalman filter. The tracking strategy relies on the identification of virtual planes tangent to vehicles bumpers. Extensive experiments are carried out in real conditions with a baseline greater than 22 meters. We achieve accurate auto-calibration at scale with cameras localization error below 28.7 cm (1.3 percent), along with reliable vehicle trajectory estimation and a mean speed error of 3.69 km/h.
Motion estimation and reconstruction of piecewise planar scenes from two views

The task of recovering the camera motion relative to the environment (motion estimation) is fundamental to many computer vision applications. We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more than one line correspondence across more than two views to recover the translation and achieves the goal by exploiting photometric constraints of the surface around the line. Experimental results on real images prove the functionality of the algorithm.
A distributed control architecture for an experimental mobile robot

The authors present an ICAR (intelligent cooperative autonomous robot) mobile robot using a distributed architecture. This mobile robot must be able to perform tasks ordered by a supervisor or another robot. A team can be built in which several ICAR agents can work in cooperation on distributed or sequential tasks. To accomplish a mission in multi-robot environments, cooperative behaviour with specific rules is necessary. After having dealt with the general interest of a such structure, the authors present the motion control devices, the sensing functions, the control architecture and the internal and external networks required for ICAR agent conception. They present the communication strategy of the external network, and the interpretation and decision strategy. In conclusion, the first results obtained from a such structure are detailed.
Omnidirectional image processing using geodesic metric

Due to distorsions of catadioptric sensors, omnidirectional images can not be treated as classical images. If the equivalence between central catadioptric images and spherical images is now well known and used, spherical analysis often leads to complex methods particularly tricky to employ. In this paper, we propose to derive omnidirectional image treatments by using geodesic metric. We demonstrate that this approach allows to adapt efficiently classical image processing to omnidirectional images.
Event detection based on "common fate" principle: application to vehicles detection from aerial sequences of road traffic

This paper introduces a vision system for road traffic surveillance from sequences acquired from an unmanned aerial vehicle (UAV). During the navigation of the UAV, the vision system acquires sequences which are treated in order to detect vehicles. The dynamic behavior of the UAV-camera system makes a fixed background impossible : we present a new approach based on the "common fate" principle : image primitives which have the same type of movement (or displacement) are grouped. The detection of vehicles is then based on the spatiotemporal grouping of primitives formulated as a normalized cuts problem. A verification step based on the Dempster-Shafer theory is also proposed in order to recognize vehicles.
Translation estimation for single viewpoint cameras using lines

We present a translation estimation method for single viewpoint (SVP) cameras using line features. Images captured by multiple central cameras such as perspective, central catadioptric and fisheye cameras are mapped to spherical images using the unified projection model. It is possible to recover the camera rotations using vanishing points of parallel line sets. We then estimate the translations from known rotations and line images on the spheres. The algorithm has been validated on simulated data and real images. This vision-based estimation approach can be applied in navigation of autonomous robots besides the conventional devices such as Global Positioning System (GPS) and Inertial Navigation System (INS). It helps vision-based localization of a single robot or recovery of relative positions among multiple robots equipped with different types of cameras.
LMI-based 2D-3D registration: From uncalibrated images to Euclidean scene

This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates, and two or more uncalibrated cameras. An unknown subset of the scanned points have their image projections detected and matched across images. The proposed approach assumes the cameras only known in some arbitrary projective frame and no calibration or autocalibration is required. The devised solution is based on a Linear Matrix Inequality (LMI) framework that allows simultaneously estimating the projective transformation relating the cameras to the scene and establishing 2D-3D correspondences without triangulating image points. The proposed LMI framework allows both deriving triangulation-free LMI cheirality conditions and establishing putative correspondences between 3D volumes (boxes) and 2D pixel coordinates. Two registration algorithms, one exploiting the scene's structure and the other concerned with robustness, are presented. Both algorithms employ the Branch-and-Prune paradigm and guarantee convergence to a global solution under mild initial bound conditions. The results of our experiments are presented and compared against other approaches.
A novel global image description approach for long term vehicle localization

Long-term place recognition for vehicles or robots in outdoor environment is still a tackling issue: numerous changes occur in appearance due to illumination variations or weather phenomena for instance, when using visual sensors. Few methods from the literature try to manage different visual sources while it could favor data interoperability across variable sensors. In this paper, we emphasis our works on cases where there is a need to associate data from different imaging sources (optics, sensors size and even spectral ranges). We developed a method with a first camera which composes the visual memory. Afterwards, we consider another camera which partially covers the same journey. Our goal is to associate live images to the prior visual memory thanks to visual features invariant to sensors changes, with the help of a probabilistic approach for the implementation part.
UAV altitude estimation by mixed stereoscopic vision

Altitude is one of the most important parameters to be known for an Unmanned Aerial Vehicle (UAV) especially during critical maneuvers such as landing or steady flight. In this paper, we present mixed stereoscopic vision system made of a fish-eye camera and a perspective camera for altitude estimation. Contrary to classical stereoscopic systems based on feature matching, we propose a plane sweeping approach in order to estimate the altitude and consequently to detect the ground plane. Since there exists a homography between the two views and the sensor being calibrated and the attitude estimated by the fish-eye camera, the algorithm consists then in searching the altitude which verifies this homography. We show that this approach is robust and accurate, and a CPU implementation allows a real time estimation. Experimental results on real sequences of a small UAV demonstrate the effectiveness of the approach.
Line based motion estimation and reconstruction of piece-wise planar scenes

We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more that one line correspondence across more than two views to recover the translation and achieves the goal by exploiting photometric constraints of the surface around the line. Experimental results on real images prove the functionality of the algorithm.
Vehicle Positioning in Road Networks without GPS

Estimating vehicle position on road maps is important for many ITS applications. Advanced Driver Assistance System (ADAS) may expect robust positioning invariant to day time, weather or simplifications induced by the topological representations of road maps. This paper describes a particle filter approach used to achieve vehicle positioning on freely available road maps. Anti-lock Braking System (ABS) and Electronic Stability Program (ESP) sensor data are used in the motion update model. Measurement updates solely rely on vehicle heading. As our results indicate, our approach is able to cope with the odometry error accumulation. We also found that our methodology is able to successfully localize and track a vehicle with a median error of 3.6m in a road network made of 380km of drivable roads with a performance comparable to a high-end INS unit.
Calibration of the omnidirectional vision sensor: SYCLOP

We present a method to calibrate the omnidirectional sensor used in our laboratory, named SYCLOP (conic system for localization and perception). This system, which is able to capture a panoramic image of a 2/spl pi/ radian field, consists of a CCD camera and a vertically oriented conic shaped reflector. In order to have a better precision than that obtained in classical applications using this kind of sensors, we consider the importance of calibration for the whole sensor. After having briefly recalled the theoretical framework used in hard calibration, we design the different transformations made between world object, cone reflector and pictures, as well as the different types of relationship between the world, the cone, the camera and the image coordinates. Finally, we present results obtained with the SYCLOP simulator and an experiment.
Obstacle detection using unsynchronized multi-camera network

In this paper, we present a simple algorithm for obstacle detection, road surface extraction and tracking using Kalman filter and u-v-disparity images. The proposed approach is based on the use of an unsynchronized camera system and the use of sparse maps instead of dense ones due to the unsynchronization constraint. First, a sparse disparity map is computed from two images then the u-v-disparity images are built from it. Road and obstacles are extracted using a modified Hough transform. Our experimental results on real images show the efficiency of our algorithm.
A new approach for face detection with omnidirectional sensors

This paper tackles the problem of frontal face detection on omnidirectional vision sensors. The adaptation of traditional face detection frameworks for our system enables to speed up the detection process while achieving good performance. We propose a method to synthesize omnidirectional image database. Hence, we were able to train our detector on the synthesized omnidirectional image database. Our experiments suggest that despite non linear distortions, our face detection algorithm has high success rate. We studied performance of Adaboost algorithm variants for our system. The proposed approach is able to match the speed and robustness of existing face detection algorithm for conventional cameras.
Robust and Optimal Sum-of-Squares-Based Point-to-Plane Registration of Image Sets and Structured Scenes

This paper deals with the problem of registering a known structured 3D scene and its metric Structure-from-Motion (SfM) counterpart. The proposed work relies on a prior plane segmentation of the 3D scene and aligns the data obtained from both modalities by solving the point-to-plane assignment problem. An inliers-maximization approach within a Branch-and-Bound (BnB) search scheme is adopted. For the first time in this paper, a Sum-of-Squares optimization theory framework is employed for identifying point-to-plane mismatches (i.e. outliers) with certainty. This allows us to iteratively build potential inliers sets and converge to the solution satisfied by the largest number of point-to-plane assignments. Furthermore, our approach is boosted by new plane visibility conditions which are also introduced in this paper. Using this framework, we solve the registration problem in two cases: (i) a set of putative point-to-plane correspondences (with possibly overwhelmingly many outliers) is given as input and (ii) no initial correspondences are given. In both cases, our approach yields outstanding results in terms of robustness and optimality.
Central catadioptric line matching for robotic applications

This paper presents a method for catadioptric line matching across multiple images. While most of previous works deals with vertical lines and planar motion, our approach is able to match any kind of lines between two views separated by a rigid transformation without any prior knowledge of the epipolar geometry. Catadioptric lines are represented by their normals in sphere space and we use only these normals and their relative positions in order to perform the matching. A geometric hashing approach allows in the first image to construct hashing tables based on bases defined by every possible couples of normals. In the second image, a voting scheme permits to select the best corresponding bases and subsequently to match catadioptric lines.We show that the proposed representation is invariant in the case of a pure rotation and quasi-invariant for a combination of rotation and translation. We also propose different experimental results obtained in real time on real outdoor sequences.
Object detection using perceptual organization and prediction/verification of hypotheses

The authors describe an approach to perceptual organization and prediction/verification of hypotheses for detecting a polyhedral 3-D object in indoor scenes. This method is a sequence of different stages which allow a reduction in the space of research and to give semantic significance to each primitive at different levels of representation. Since in indoor environments most of the objects are man-made, it appears that their morphological characteristics are essentially linear and regular, and for these reasons straight line segments are the best primitives available in order to detect such objects. From these primitives, geometric relations allow to obtain different classes of pair of segments which represent a second level in their algorithm. Next a method based on prediction and verification of hypotheses with score assignment is performed in order to create the third level, constituted by different simple geometric shapes as squares, rectangles, triangles and other polygons. Last stage is dedicated to the grouping of these different shapes by perceptual organization for finding complex objects in the scene. After a more detailed description of their algorithm, the authors prove that the method is both efficient and robust in the presence of noise like occlusion. Finally, experimental results obtained in the laboratory are shown in order to demonstrate the method's effectiveness.
Robust Attitude Estimation with Catadioptric Vision

Attitude (roll and pitch) is an essential data for the navigation of a UAV. Rather than using inertial sensors, we propose a catadioptric vision system allowing a fast, robust and accurate estimation of these angles. We show that the optimization of a sky/ground partitioning criterion associated with the specific geometric characteristics of the catadioptric sensor provides very interesting results. Experimental results obtained on real sequences are presented and compared with inertial sensor measures
Robust Radial Face Detection for Omnidirectional Vision

Bio-inspired and non-conventional vision systems are highly researched topics. Among them, omnidirectional vision systems have demonstrated their ability to significantly improve the geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the picture. In this paper, we investigate what must be taken into account and how to process omnidirectional images provided by the sensor. We focus our research on face detection and highlight the fact that particular attention should be paid to the descriptors in order to successfully perform face detection on omnidirectional images. We demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the adaptation of existing object-detection frameworks, designed for perspective images, should be focused on the choice of appropriate image descriptors in the design of the object-detection pipeline.
Automatic calibration of catadioptric cameras in urban environment

Camera calibration is an important step for vision-based stabilization of unmanned aerial vehicles (UAV). The goal of this paper is to develop a method for automatic calibration of a catadioptric camera so that it can be easily run before mounting the camera on the UAV or even during the flight to deal with vibrations or shocks. Whereas existing works can provide interesting results, they suffer from several practical limitations (manual line extraction, inaccurate conic fitting, calibration pattern, camera motion, execution time, etc...) and therefore cannot be applied in our application. The proposed algorithm aims to determine the most probable calibration that verifies some geometric constraints induced by catadioptric projection. In order to efficiently maximize this probability, we use a particle filtering approach. Experimental results have demonstrated the effectiveness of the proposed method.
UAV Attitude Computation by Omnidirectional Vision in Urban Environment

Attitude is one of the most important parameters for a UAV during a flight. Attitude computation methods based vision generally use the horizon line as reference. However, the horizon line becomes an inadequate feature in urban environment. We then propose in this paper an omnidirectional vision system based on straight lines (very frequent in urban environment) that is able to compute the roll and pitch angles. The method consists in finding bundles of horizontal and vertical parallel lines in order to obtain an absolute reference for the attitude computation. We also develop here a new and efficient method for line extraction and bundle of parallel line detection. An original method of horizontal and vertical plane detection is also provided. We show experimental results on different images extracted from video sequences.
Analysis of low-altitude aerial sequences for road traffic diagnosis using graph partitioning and Markov hierarchical models

This article focuses on an original approach aiming the processing of low-altitude aerial sequences taken from an helicopter (or drone) and presenting a road traffic. Proposed system attempts to extract vehicles from acquired sequences. Our approach begins with detecting the primitives of sequence images. At the time of this step of segmentation, the system computes dominant motion for each pair of images. This motion is computed using wavelets analysis on optical flow equation and robust techniques. Interesting areas (areas not affected by the dominant motion) are detected thanks to a Markov hierarchical model. Primitives stemming from segmentation and interesting areas are used to build a graph on which partitioning process is executed. This graph gathers only the primitives (considered as nodes) witch belong to the interesting areas. Nodes are interconnected by Perceptive Criteria. To extract the important elements of the sequence (vehicles), a bi-partition of this graph using Normalized Cuts technique takes place. Finally, parameters of proposed algorithm are chosen thanks to a learning stage for which we use Genetic Algorithms.