nils-herrmann/clean_scirepeval huggingface dataset:
Visual Learning for Landmark Recognition

Recognizing landmark is a critical task for mobile robots. Landmarks are used for robot positioning, and for building maps of unknown environments. In this context, the traditional recognition techniques based on strong geometric models cannot be used. Rather, models of landmarks must be built from observations using image-based visual learning techniques. Beyond its application to mobile robot navigation, this approach addresses the more general problem of identifying groups of images with common attributes in sequences of images. We show that, with the appropriate domain constraints and image descriptions, this can be done using efficient algorithms as follows: Starting with a "training" sequence of images, we identify groups of images corresponding to distinctive landmarks. Each group is described by a set of feature distributions. At run-time, the observed images are compared with the sets of models in order to recognize the landmarks in the input stream.
SMARTY: Point-Based Range Processing for Autonomous Driving

A critical component of mobile robot systems is the ability to detect regions of the environment in which the vehicle should not travel. This is an especially important problem when a robot operates outdoors at relatively high continuous speed. In on-road driving, obstacles have to be detected at long range and very quickly to allow enough reaction time at high speeds. In cross-country driving, unsafe regions of the terrain also have to be identified at long range with the added requirement that simple strategies such as elevation thresholds do not work because of the complexity of the terrain. Without making any distinction, we will refer to regions of the environment in which the vehicle is not allowed to drive as obstacle regions or as untraversable regions.
Multi-scale classification of 3-D objects

We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced. Given a library of models, objects that are similar based on this multi-scale measure are grouped together into classes. The objects that are in the same class are combined into a single prototype object. Finally, the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. Beyond its application to object recognition, this approach provides an attractive implementation of the intuitive notions of scale and approximate similarity for 3-D shapes.
A Navigation System for Goal Acquisition in Unknown Environments

The cross-country navigation system described in the previous chapter assumes that intermediate goal points have been defined between the starting position and the final goal position. Placement of goal points assumes implicitly that some knowledge of the environment, for example in the form of a map, was available prior to the mission. This knowledge may not have been available, may have been incomplete, and may have been erroneous. In such cases, the system must be able to map its environment on the fly and re-compute its path as new information is added to its map. This chapter describes how, by combining the SMARTY system for obstacle detection and avoidance described in Chapter 6 with the D* planning system described in Chapter 11, we are able to conduct long-range autonomous missions with dynamic map building and path planning.
A system for semi-automatic modeling of complex environments

We present a perception system, called Artisan, that semi-automatically builds 3-D models of a robot's workspace. Range images are acquired with a scanning laser rangefinder and then processed, based an a systematic sensor characterization, to remove noise and artifacts. Complex 3-D objects represented as surface meshes are subsequently recognized in the range images and inserted into a virtual workspace. This graphical virtual workspace is then used to by human operators to plan and execute remote robotic operations.
Field Trials of a Prototype Lunar Rover under Multi-Sensor Safeguarded Teleoperation Control

This paper presents the results of field trials of a prototype lunar rover traveling over natural terrain under safeguarded teleoperation control. Both the rover and the safeguarding approach have been used in previous work. The original contributions of this paper are the development and integration of a laser sensing system, and extensive field testing of the overall system. The laser system, which complements an existing stereo vision system, is based on a line-scanning laser ranger viewing the area 1 meter in front of the rover. The laser system has demonstrated excellent performance: zero misses and few false alarms operating at 4 Hz. The overall safeguarding system guided the rover 43 km over lunar analogue terrain with 0.8 failures per kilometer.
Recognizing objects by matching oriented points

We present an approach to recognition of complex objects in cluttered 3-D scenes that does not require feature extraction or segmentation. Our object representation comprises descriptive images associated with each oriented point on the surface of an object. Using a single point basis constructed from an oriented point, the position of other points on the surface of the object can be described by two parameters. The accumulation of these parameters for many points on the surface of the object results in an image at each oriented point. These images, localized descriptions of the global shape of the object, are invariant to rigid transformations. Through correlation of images, point correspondences between a model and scene data are established and then grouped using geometric consistency. The effectiveness of our algorithm is demonstrated with results showing recognition of complex objects in cluttered scenes with occlusion.
Surface registration by matching oriented points

For registration of 3-D free-form surfaces we have developed a representation which requires no knowledge of the transformation between views. The representation comprises descriptive images associated with oriented points on the surface of an object. Constructed using single point bases, these images are data level shape descriptions that are used for efficient matching of oriented points. Correlation of images is used to establish point correspondences between two views; from these correspondences a rigid transformation that aligns the views is calculated. The transformation is then refined and verified using a modified iterative closest point algorithm. To demonstrate the generality of our approach, we present results from multiple sensing domains.
Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon

From the Publisher: 
Intelligent Unmanned Ground Vehicles describes the technology developed and the results obtained by the Carnegie Mellon Robotics Institute in the course of the DARPA Unmanned Ground Vehicle (UGV) project. The goal of this work was to equip off-road vehicles with computer-controlled, unmanned driving capabilities. The book describes contributions in the area of mobility for UGVs including: tools for assembling complex autonomous mobility systems; on-road and off-road navigation; sensing techniques; and route planning algorithms. In addition to basic mobility technology, the book covers a number of integrated systems demonstrated on the field in realistic scenarios. The approaches presented in this book can be applied to a wide range of mobile robotics applications, from automated passenger cars to planetary exploration, construction and agricultural machines.
An Integral Approach to Free-Form Object Modeling

Presents an approach to free-form object modeling from multiple range images. In most conventional approaches, successive views are registered sequentially. In contrast to the sequential approaches, we propose an integral approach which reconstructs statistically optimal object models by simultaneously aggregating all data from multiple views into a weighted least-squares (WLS) formulation. The integral approach has two components. First, a global resampling algorithm constructs partial representations of the object from individual views, so that correspondence can be established among different views. Second, a weighted least-squares algorithm integrates resampled partial representations of multiple views, using the techniques of principal component analysis with missing data (PCAMD). Experiments show that our approach is robust against noise and mismatch.
On 3D Shape Synthesis

We present a novel approach to 3D shape synthesis of closed surfaces. A curved or polyhedral 3D object of genus zero is represented by a curvature distribution on a spherical mesh that has nearly uniform distribution with known connectivity among mesh nodes. This curvature distribution, i.e., the result of forward mapping from shape space to curvature space, is used as the intrinsic shape representation because it is invariant to rigid transformation and scale factor. Furthermore, with regularity constraints on the mesh, the inverse mapping from curvature space to shape space always exists and can be recovered using an iterative method. Therefore, the task of synthesizing a new shape from two known objects becomes one of interpolating the two known curvature distributions,and then mapping the interpolated curvature distribution back to a 3D morph. Using the distance between two curvature distributions, we can quantitatively control the shape synthesis process to yield smooth curvature migration. Experiments show that our method produces smooth and realistic shape morphs.
On 3D shape similarity

This paper addresses the problem of 3D shape similarity between closed surfaces. A curved or polyhedral 3D object of genus zero is represented by a mesh that has nearly uniform distribution with known connectivity among mesh nodes. A shape similarity metric is defined based on the L/sub 2/ distance between the local curvature distributions over the mesh representations of the two objects. For both convex and concave objects, the shape metric can be computed in time O(n/sup 2/), where n is the number of tessellations of the sphere or the number of meshes which approximate the surface. Experiments show that our method produces good shape similarity measurements.
Object representation in computer vision II : ECCV '96 International Workshop, Cambridge, U.K., April 13-14, 1996 : proceedings

Report on the 1996 international workshop on object representation in computer vision.- From an intensity image to 3-D segmented descriptions.- Recovering Generalized Cylinders by monocular vision.- Combinatorial geometry for shape representation and indexing.- Representing objects using topology.- Curvature based signatures for object description and recognition.- On 3D shape synthesis.- Shape constancy in pictorial relief.- Dimensionality of illumination manifolds in appearance matching.- Learning object representations from lighting variations.- Learning appearance models for object recognition.- An image oriented CAD approach.- An experimental comparison of appearance and geometric model based recognition.- Virtualized reality: Being mobile in a visual scene.- Generic shape learning and recognition.- A hybrid approach to 3D representation.- Finding pictures of objects in large collections of images.- Beyond the Hough transform: Further properties of the R? mapping and their applications.- Non-Euclidean object representations for calibration-free video overlay.
Seafloor map generation for autonomous underwater vehicle navigation

Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.
A complete navigation system for goal acquisition in unknown environments

Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, we have developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect obstacles and recommend steering commands to ensure robot safety. These obstacles are passed to the global system which stores them in a map of the environment. With each addition to the map, the global system uses an incremental path planning algorithm to optimally replan the global path and recommend steering commands to reach the goal. An arbiter combines the steering recommendations to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.
Building 3-D Models from Unregistered Range Images

In this paper, we describe a new approach for building a three-dimensional model from a set of range images. The approach is able to build models of free-form surfaces obtained from arbitrary viewing directions, with no initial estimate of the relative viewing directions. The approach is based on building discrete meshes representing the surfaces observed in each of the range images, mapping each of the meshes to a spherical image, and computing the transformations between the views by matching the spherical images, The meshes are built using an iterative fitting algorithm previously developed; the spherical images are built by mapping the nodes of the surface meshes to the nodes of a reference mesh on the unit sphere and by storing a measure of curvature at every node. We describe the algorithms used for building such models from range images and for matching them. We show results obtained using range images of complex objects.
Weakly-calibrated stereo perception for rover navigation

Presents a vision system for autonomous navigation based on stereo perception without 3D reconstruction. This approach uses weakly calibrated stereo images, i.e. images for which only the epipolar geometry is known. The vision system first rectifies the images, matches selected points between the two images, and then computes the relative elevation of the points relative to a reference plane as well as the images of their projections on this plane. We have integrated this vision module into a complete navigation system. In this system, the relative elevation is used as a shape indicator in order to compute appropriate steering directions everytime a new stereo pair is processed. We have conducted initial experiments in unstructured, outdoor environments with an wheeled rover.<<ETX>>
A complete navigation system for goal acquisition in unknown environments

Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, the authors developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect and avoid obstacles. The global system uses an incremental path planning algorithm to optimally replan the global path for each detected obstacle. A control arbiter steers the robot to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.
An integral approach to free-formed object modeling

Presents a new approach to free-formed object modeling from multiple range images. In most conventional approaches, successive views are registered sequentially. In contrast to the sequential approaches, we propose an integral approach which reconstructs statistically optimal object models by simultaneously aggregating all data from multiple views into a weighted least-squares (WLS) formulation. The integral approach has two components. First, a global resampling algorithm constructs partial representations of the object from individual views so that correspondences can be established among different views. The global resampling algorithm is based on the spherical attribute image (SAI) previously introduced in the context of object representation and recognition. Second, a weighted least-squares algorithm integrates resampled partial representations of multiple views, using the technique of principal component analysis with missing data (PCAMD). Experiments using real range images show that our approach is robust against noise and mismatches, and generates accurate object models.<<ETX>>
Object representation in computer vision : International NSF-ARPA Workshop, New York City, NY, USA, December 5-7, 1994 : proceedings

Report on the 1995 workshop on 3-D object representations in computer vision.- Object recognition: The search for representation.- Appearance-based 3D object recognition.- Using quasi-invariants for automatic model building and object recognition: An overview.- Object representation for recognition-by-alignment.- Distinctive representations for the recognition of curved surfaces using outlines and markings.- The epipolar parametrization.- Using two-dimensional models to interact with the three-dimensional world.- Representations for recognizing complex curved 3D objects.- On representation and invariant recognition of complex objects based on patches and parts.- Algebraic geometry and object representation in computer vision.- Discrete surface signal processing: The polygon as the surface element.- Spline representations in 3-D vision.- Triangles as a primary representation.- Body-centered representation and perception.- The challenge of generic object recognition.- A physics-based framework for segmentation, shape and motion estimation.- Modal represenations.- Time representation of deformations: Combining vibration modes and Fourier analysis.- Physics in a fantasy world vs robust statistical estimation.- Towards a robust physics-based object recognition system.- Toward non-parametric digital shape representation and recovery.- Spherical representations: From EGI to SAI.- From physics-based representation to functional modeling of highly complex objects.
Refinement of Seafloor Elevation Using Acoustic Backscatter

We propose an algorithm for the reconstruction of elevation and material property maps of the seafloor using a sidescan sonar backscatter image and sparse bathymetric points co-registered within the image. Given a path for the sensor; the reconstruction is corrected for the movement of the fish during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The elevation surface and the scattering parameter maps converge to their best fit shape and values given the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system and performing the reconstruction again. The algorithm supports different scattering models, so it can be applied to different underwater environments and sonar sensors. In addition to the elevation map of the seafloor, the parameters of the scattering model at every point in the image are generated. Since these parameters describe material properties of the seafloor, the maps of the scattering model parameters can be used to segment the seafloor by material type.
A Spherical Representation for Recognition of Free-Form Surfaces

Introduces a new surface representation for recognizing curved objects. The authors approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. The authors define local regularity constraints that the mesh must satisfy. The authors then define a canonical mapping between the mesh describing the object and a standard spherical mesh. A surface curvature index that is pose-invariant is stored at every node of the mesh. The authors use this object representation for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. The authors show how the similarity between reference model and observed data can be evaluated and they show how the pose of the reference object in the observed scene can be easily computed using this representation. The authors present results on real range images which show that this approach to modelling and recognizing 3D objects has three main advantages: (1) it is applicable to complex curved surfaces that cannot be handled by conventional techniques; (2) it reduces the recognition problem to the computation of similarity between spherical distributions; in particular, the recognition algorithm does not require any combinatorial search; and (3) even though it is based on a spherical mapping, the approach can handle occlusions and partial views.