Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

Semi-automatic geometric correction of airborne hyperspectral push-broom images using ground control points and linear features

A semi-automatic geometric correction method is presented for high-resolution airborne hyperspectral push-broom images in this paper. The method mainly consists of a correction model based on ground control points and linear features as well as a semi-automatic extraction procedure for linear features. With a panchromatic image as the reference map, the distorted hyperspectral image can be calibrated using ground control points and linear features digitized on both the map and the distorted image. In particular, the ground control points are chosen manually, and the linear features can be extracted semi-automatically through the combination of region segmentation based on the region-scalable fitting energy model and a further correction procedure. The experimental results of two hyperspectral images demonstrate that our method can achieve visually well-rectified images and high calibration accuracy.     

Matching aerial images to 3-D terrain maps

A terrain-matching algorithm is presented for use in a passive aircraft navigation system. A sequence of aerial images is matched to a reference digital map of the 3-D terrain. Stereo analysis of successive images results in a recovered elevation map. A cliff map is then used as a novel compact representation of the 3-D surfaces. The position and heading of the aircraft are determined with a terrain-matching algorithm that locates the unknown cliff map within the reference cliff map. The robustness of the matching algorithm is demonstrated by experimental results using real terrain data     

High-resolution terrain map from multiple sensor data

The authors present 3-D vision techniques for incrementally building an accurate 3-D representation of rugged terrain using multiple sensors. They have developed the locus method to model the rugged terrain. The locus method exploits sensor geometry to efficiently build a terrain representation from multiple sensor data. The locus method is used to estimate the vehicle position in the digital elevation map (DEM) by matching a sequence of range images with the DEM. Experimental results from large-scale real and synthetic terrains demonstrate the feasibility and power of the 3-D mapping techniques for rugged terrain. In real world experiments, a composite terrain map was built by merging 125 real range images. Using synthetic range images, a composite map of 150 m was produced from 159 images. With the proposed system, mobile robots operating in rugged environments can build accurate terrain models from multiple sensor data     

Mobile robot relocation from echolocation constraints

This paper presents a method for relocation of a mobile robot using sonar data. The process of determining the pose of a mobile robot with respect to a global reference frame in situations where no a priori estimate of the robot's location is available is cast as a problem of searching for correspondences between measurements and an a priori map of the environment. A physically-based sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. Individual range returns are used as data features in a constraint-based search to determine the robot's position. A hypothesize and test technique is employed in which positions of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. The algorithm determines the positions which provide the best match between the range returns and the environment model. The performance of the approach is demonstrated using data from both a single scanning Polaroid sonar and from a ring of Polaroid sonar sensors     

A Stochastic Map Building Method for Mobile Robot using 2-D Laser Range Finder

This paper presents a stochastic map building method for mobile robot using a 2-D laser range finder. Unlike other methods that are based on a set of geometric primitives, the presented method builds a map with a set of obstacle regions. In building a map of the environment, the presented algorithm represents the obstacles with a number of stochastic obstacle regions, each of which is characterized by its own stochastic parameters such as mean and covariance. Whereas the geometric primitives based map sometimes does not fit well to sensor data, the presented method reliably represents various types of obstacles including those of irregular walls and sets of tiny objects. Their shapes and features are easily extracted from the stochastic parameters of their obstacle regions, and are used to develop reliable navigation and obstacle avoidance algorithms. The algorithm updates the world map in real time by detecting the changes of each obstacle region. Consequently, it is adequate for modeling the quasi-static environment, which includes occasional changes in positions of the obstacles rather than constant dynamic moves of the obstacles. The presented map building method has successfully been implemented and tested on the ARES-II mobile robot system equipped with a LADAR 2D-laser range finder.     

3-D Scene Data Recovery Using Omnidirectional Multibaseline Stereo

A traditional approach to extracting geometric information from a large scene is to compute multiple 3-D depth maps from stereo pairs or direct range finders, and then to merge the 3-D data. However, the resulting merged depth maps may be subject to merging errors if the relative poses between depth maps are not known exactly. In addition, the 3-D data may also have to be resampled before merging, which adds additional complexity and potential sources of errors.This paper provides a means of directly extracting 3-D data covering a very wide field of view, thus by-passing the need for numerous depth map merging. In our work, cylindrical images are first composited from sequences of images taken while the camera is rotated 360° about a vertical axis. By taking such image panoramas at different camera locations, we can recover 3-D data of the scene using a set of simple techniques: feature tracking, an 8-point structure from motion algorithm, and multibaseline stereo. We also investigate the effect of median filtering on the recovered 3-D point distributions, and show the results of our approach applied to both synthetic and real scenes.     

Appearance-Based Structure from Motion Using Linear Classes of 3-D Models

In this paper, we address the problem of recovering 3-D models from sequences of partly calibrated images with unknown correspondence. To that end, we integrate tracking, structure from motion with geometric constraints (specifically in the form of linear class models) in a single framework. The key to making the proposed approach work is the use of appearance-based model matching and refinement which updates the estimated correspondences on each iteration of the algorithm. Another key feature is the matching of a 3-D model directly with the input images without the conventional 2-step approach of stereo data recovery and 3-D model fitting. Initialization of the linear class model to one of the input images (the reference image) is currently partly manual.This synthesis and refine approach, or appearance-based constrained structure from motion (AbCSfm), is especially useful in recovering shapes of objects whose general structureis known but which may have little discernable texture in significant parts of their surfaces. We applied the proposed approach to 3-D face modeling from multiple images to create new 3-D faces for DECface, a synthetic talking head developed at Cambridge Research Laboratory, Digital Equipment Corporation. The DECface model comprises a collection of 3-D triangular and rectangular facets, with nodes as vertices. In recovering the DECface model, we assume that the sequence of images is taken with a camera with unknown focal length and pose. The geometric constraints used are of the form of linear combination of prototypes of 3-D faces of real people. Results of this approach show its good convergence properties and its robustness against cluttered backgrounds.     

Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation

To facilitate scene understanding and robot navigation in large scale urban environment, a two-layer enhanced geometric map(EGMap) is designed using videos from a monocular onboard camera. The 2D layer of EGMap consists of a 2D building boundary map from top-down view and a 2D road map, which can support localization and advanced map-matching when compared with standard polyline-based maps. The 3D layer includes features such as 3D road model,and building facades with coplanar 3D vertical and horizontal line segments, which can provide the 3D metric features to localize the vehicles and flying-robots in 3D space. Starting from the 2D building boundary and road map, EGMap is initially constructed using feature fusion with geometric constraints under a line feature-based simultaneous localization and mapping(SLAM) framework iteratively and progressively. Then, a local bundle adjustment algorithm is proposed to jointly refine the camera localizations and EGMap features. Furthermore, the issues of uncertainty, memory use, time efficiency and obstacle effect in EGMap construction are discussed and analyzed. Physical experiments show that EGMap can be successfully constructed in large scale urban environment and the construction method is demonstrated to be very accurate and robust.     

Reconstructing a textured CAD model of an urban environment using vehicle-borne laser range scanners and line cameras

Abstract. In this paper, a novel method is presented for generating a textured CAD model of an outdoor urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer-based navigation system. Laser range and line images are measured as the vehicle moves forward. They are synchronized with the navigation system so they can be geo-referenced to a world coordinate system. Generation of the CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features, such as buildings, ground surfaces, and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives, such as a planar face, a triangulated irregular network (TIN), and a triangle. The texture of the urban features is generated by projecting and resampling line images onto the geometric model. An outdoor experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.     

An approach for cross-data querying and spatial reasoning of tunnel alignments

In mechanized tunneling projects, finding a low-risk and cost-effective alignment is an important task. Several alignment variants are usually created and each one is intensely scrutinized. Variants often have individual advantages and disadvantages and can lead to different constructive designs of a tunnel. In order to find the best alignment possible the variants have to be analyzed and evaluated based on requirements and evaluation criteria, such as safety, cost, built environment and operational requirements. To perform this evaluation and to enable comprehensive decision making, a holistic planning environment is examined that includes documents and models of different domains. In general, these domain specific data differ schematically and semantically, which consequently makes it challenging to combine and compare such diverse data. For this purpose, information from different sources must be linked and evaluated in a structured way. In particular, spatial relationships have to be investigated. Therefore, in this paper, ontology databases are utilized to merge BIM and GIS at data level to create an integrated model of the entire tunneling project. Relevant information for decision-making can then be derived, such as the location of private and public buildings that are in a certain vicinity of the planned alignment. On the one hand, the implementation of queries is a popular and frequently used approach to check for semantic properties. On the other hand, using a query language to derive information from geometric data can be challenging, due to the necessity of processing geometric data prior to and during query execution. Additionally, geometric definitions can differ in the considered coordinate reference system, the dimension or the structure. To handle geometry information by employing query languages, representations are methodically transformed to well-known text literals. A simplified and uniform geometric representation can be utilized for spatial reasoning, for example by adopting GeoSPARQL methods.     

局部熵驱动的模糊区域竞争图像分割

针对现有图像分割模型对光照敏感,提出一种新的基于区域的主动轮廓线模型。该模型能量泛函包含一个惩罚区域弧长的几何正则项和一个区域数据拟合项,特别的是数据拟合项采用局部熵来区分不同的区域。首先,根据图像像素空间排列之间的相关性,采用一个滑动窗函数提取图像局部熵特征,将图像从灰度空间转化到相应局部熵特征空间;然后,在局部熵空间计算最大后验分割概率得出两相区域竞争模型,为了能够快速求解该模型,采用隶属度函数替换特征函数得到了凸的模糊区域竞争模型。最后,采用快速的Chambolle对偶方法得到全局最小解。实验结果表明,该算法可以得到令人满意的分割效果且收敛速度快和对光照稳定。     

Integration of a stereo vision system and a laser range finder for 3-D construction

In this article, we investigate the problem of integrating a binocular stereo vision system and a laser range finder to construct a 3-D map of the environment. The proposed scheme is realized by using the alignment parameters obtained in the 2-D map construction of the laser range finder for the 3-D data generated by the stereo vision system. The 2-D map alignment task is formulated as an optimization problem of minimizing the alignment errors between local maps and selected parts of the developing global map. The problem is then solved using the Simplex method. To increase the robustness of the searching process, multiple initial guesses are provided in the Simplex method. The performance of the proposed architecture is verified by experimental results from a mobile vehicle for obstacle avoidance.     

Optimal linear combination of facial regions for improving identification performance.

This paper presents a novel 3-D multiregion face recognition algorithm that consists of new geometric summation invariant features and an optimal linear feature fusion method. A summation invariant, which captures local characteristics of a facial surface, is extracted from multiple subregions of a 3-D range image as the discriminative features. Similarity scores between two range images are calculated from the selected subregions. A novel fusion method that is based on a linear discriminant analysis is developed to maximize the verification rate by a weighted combination of these similarity scores. Experiments on the Face Recognition Grand Challenge V2.0 dataset show that this new algorithm improves the recognition performance significantly in the presence of facial expressions.     

Fusion of range camera and photogrammetry: a systematic procedure for improving 3-D models metric accuracy

The generation of three-dimensional (3-D) digital models produced by optical technologies in some cases involves metric errors. This happens when small high-resolution 3-D images are assembled together in order to model a large object. In some applications, as for example 3-D modeling of Cultural Heritage, the problem of metric accuracy is a major issue and no methods are currently available for enhancing it. The authors present a procedure by which the metric reliability of the 3-D model, obtained through iterative alignments of many range maps, can be guaranteed to a known acceptable level. The goal is the integration of the 3-D range camera system with a close range digital photogrammetry technique. The basic idea is to generate a global coordinate system determined by the digital photogrammetric procedure, measuring the spatial coordinates of optical targets placed around the object to be modeled. Such coordinates, set as reference points, allow the proper rigid motion of few key range maps, including a portion of the targets, in the global reference system defined by photogrammetry. The other 3-D images are normally aligned around these locked images with usual iterative algorithms. Experimental results on an anthropomorphic test object, comparing the conventional and the proposed alignment method, are finally reported.     

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments.     

Geometric Information Criterion for Model Selection

In building a 3-D model of the environment from image and sensor data, one must fit to the data an appropriate class of models, which can be regarded as a parametrized manifold, or geometric model, defined in the data space. In this paper, we present a statistical framework for detecting degeneracies of a geometric model by evaluating its predictive capability in terms of the expected residual and derive the geometric AIC. We show that it allows us to detect singularities in a structure-from-motion analysis without introducing any empirically adjustable thresholds. We illustrate our approach by simulation examples. We also discuss the application potential of this theory for a wide range of computer vision and robotics problems.