nils-herrmann/clean_scirepeval huggingface dataset:
Provably-convergent iterative methods for projective structure from motion

The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to ensure that the optimization process. is well posed. This paper examines three plausible choices, and shows that the first one leads to the Sturm-Triggs projective factorization algorithm, while the other two lead to new provably-convergent approaches. Experiments with synthetic and real data are used to compare the proposed techniques to the Sturm-Triggs algorithm and bundle adjustment.
Object recognition using boosted discriminants

We approach the task of object discrimination as that of learning efficient "codes" for each object class in terms of responses to a set of chosen discriminants. We formulate this approach in an energy minimization framework. The "code" is built incrementally by successively constructing discriminants that focus on pairs of training images of objects that are currently hard to classify. The particular discriminants that we use partition the set of objects of interest into two well-separated groups. We find the optimal discriminant as well as partition by formulating an objective criteria that measures the well-separateness of the partition. We derive an iterative solution that alternates between the solutions for two generalized eigenproblems, one for the discriminant parameters and the other for the indicator variables denoting the partition. We show how the optimization can easily be biased to focus on hard to classify pairs, which enables us to choose new discriminants one by one in a sequential manner We validate our approach on a challenging face discrimination task using parts as features and show that it compares favorably with the performance of an eigenspace method.
Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.
Iterative projective reconstruction from multiple views

We propose an iterative method for the recovery of the projective structure and motion from multiple images. It has been recently noted that by scaling the measurement matrix by the true projective depths, recovery of the structure and motion is possible by factorization. The reliable determination of the projective depths is crucial to the success of this approach. The previous approach recovers these projective depths using pairwise constraints among images. We first discuss a few important drawbacks with this approach. We then propose an iterative method where we simultaneously recover both the projective depths as well as the structure and motion that avoids some of these drawbacks by utilizing all of the available data uniformly. The new approach makes use of a subspace constraint on the projections of a 3D point onto an arbitrary number of images. The projective depths are readily determined by solving a generalized eigenvalue problem derived from the subspace constraint. We also formulate a dual subspace constraint on all the points in a given image, which can be used for verifying the projective geometry of a scene or object that was modeled. We prove the monotonic convergence of the iterative scheme to a local maximum. We show the robustness of the approach on both synthetic and real data despite large perspective distortions and varying initializations.
Experimental Comparison of Techniques for Localization and Mapping Using a Bearing-Only Sensor

We present a comparison of an extended Kalman filter and an adaptation of bundle adjustment from computer vision for mobile robot localization and mapping using a bearing-only sensor. We show results on synthetic and real examples and discuss some advantages and disadvantages of the techniques. The comparison leads to a novel combination of the two techniques which results in computational complexity near Kalman filters and performance near bundle adjustment on the examples shown.
Invariant filtering for simultaneous localization and mapping

This paper presents an algorithm for simultaneous localization and map building for a mobile robot moving in an unknown environment. The robot can measure only the bearings to identifiable targets and its own relative motion. The approach is to recursively estimate features of the environment which are invariant to the robot pose in order to decouple the pose error from the map error. The highly nonlinear nature of this problem requires more explicit reasoning about the spatial relationships between landmarks and between the robot and landmarks than those used in previous methods.
3D map reconstruction from range data

We present techniques for building models of complex environments from range data gathered at multiple viewpoints. The challenges in this problem are: the matching of unregistered views without prior knowledge of pose, the use of very large data sets, and the manipulation of data sets of different resolutions and from different sensors. Our approach is unique in that no prior knowledge of the relative viewpoints is needed in order to register the data. We show results in building maps of interior environment from range finding data, building large terrain maps from ground-based and from aerial data, and from an operational for mapping from stereo data for hazardous environment characterization. The paper summarizes the major results obtained so far in this area.
Active and passive range sensing for robotics

In this paper, we present a brief survey of the technologies currently available for range sensing, of their use in robotics applications, and of emerging technologies for future systems. The paper is organized by type of sensing: laser range finders, triangulation range finders, and passive stereo. A separate section focuses on current development in the area of nonscanning sensors, a critical area to achieve range sensing performance comparable to that of conventional cameras. The presentation of the different technologies is based on many recent examples from robotics research.
First results in omni-directional visual servoing

In this paper, we describe a visual servoing system developed as a human-robot interface to drive a mobile robot toward any chosen target. An omni-directional camera is used to get the 360° of field of view and an efficient tracking technique is developed to track the target. The use of the omni-directional geometry eliminates many of the problems common in visual tracking and makes the use of visual servoing a practical alternative for robot-human interaction. The experiments demonstrate that it is an effective and robust way to guide a robot. In particular, the experiments show robustness of the tracker to loss of template, vehicle motion, and change in scale and orientation.
Omni-directional structure from motion

In this paper, we describe our work on a robot navigation system using an omnidirectional camera as the primary sensor. An omnidirectional structure from motion algorithm is presented with its uncertainty analyzed. We evaluate the omnidirectional SFM on both synthetic data and on real image sequences. Comparison with the conventional camera is made and we show that in certain situations the omnidirectional SFM gives better results than the conventional one by taking advantage of its larger field of view.
Harmonic Shape Images: A Representation for 3D Free-Form Surfaces Based on Energy Minimization

A new representation called Harmonic Shape Images for 3D free-form surfaces is proposed in this paper. This representation is based on the well-studied theory of Harmonic Maps which studies the mapping between different metric manifolds from the energyminimization point of view. The basic idea of Harmonic Shape Images is to map a 3D surface patch with disc topology to a 2D domain and encode the shape information of the surface patch into the 2D image. Due to the application of harmonic maps in generating Harmonic Shape Images, Harmonic Shape Images have the following advantages: they preserve both the shape and the continuity of the underlying surfaces, they are robust to occlusion and they are independent of surface sampling strategy. The proposed representation is applied to solve the surface-registration problem. Experiments have been conducted on real data and results are presented in the paper.
Harmonic maps and their applications in surface matching

The surface-matching problem is investigated in this paper using a mathematical tool called harmonic maps. The theory of harmonic maps studies the mapping between different metric manifolds from the energy-minimization point of view. With the application of harmonic maps, a surface representation called harmonic shape images is generated to represent and match 3D freeform surfaces. The basic idea of harmonic shape images is to map a 3D surface patch with disc topology to a 2D domain and encode the shape information of the surface patch into the 2D image. This simplifies the surface-matching problem to a 2D image-matching problem. Due to the application of harmonic maps in generating harmonic shape images, harmonic shape images have the following advantages: they have sound mathematical background; they preserve both the shape and continuity of the underlying surfaces; and they are robust to occlusion and independent of any specific surface sampling scheme. The performance of surface matching using harmonic maps is evaluated using real data. Preliminary results are presented in the paper.
Evolution of a Prototype Lunar Rover: Addition of Laser-Based Hazard Detection, and Results from Field Trials in Lunar Analog Terrain

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 hazard detection 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.
E cient Recovery of Low-dimensional Structure from High-dimensional Data

Many modeling tasks in computer vision. e.g. structure from motion, shape/re ectance from shading, lter synthesis have a low-dimensional intrinsic structure even though the dimension of the input data can be relatively large. We propose a simple but surprisingly e ective iterative randomized algorithm that drastically cuts down the time required for recovering the intrinsic structure. The computational cost depends only on the intrinsic dimension of the structure of the task. It is based on the recently proposed Cascade Basis Reduction (CBR) algorithm that was developed in the context of steerable lters. A key feature of our algorithm compared with CBR is that an arbitrary apriori basis for the task is not required. This allows us to extend the applicability of the algorithm to tasks beyond steerable lters such as structure from motion. We prove the convergence for the new algorithm. In practice the new algorithm is much faster than CBR for the same modeling error. We demonstrate this speed-up for the construction of a steerable basis for Gabor lters. We also demonstrate the generality of the new algorithm by applying it to to an example from structure from motion without missing data.
Efficient recovery of low-dimensional structure from high-dimensional data

Many modeling tasks in computer vision, e.g. structure from motion, shape/reflectance from shading, filter synthesis have a low-dimensional intrinsic structure even though the dimension of the input data can be relatively large. We propose a simple but surprisingly effective iterative randomized algorithm that drastically cuts down the time required for recovering the intrinsic structure. The computational cost depends only on the intrinsic dimension of the structure of the task. It is based on the recently proposed Cascade Basis Reduction (CBR) algorithm that was developed in the context of steerable filters. A key feature of our algorithm compared with CBR is that an arbitrary a priori basis for the task is not required. This allows us to extend the applicability of the algorithm to tasks beyond steerable filters such as structure from motion. We prove the convergence for the new algorithm. In practice the new algorithm is much faster than CBR for the same modeling error. We demonstrate this speed-up for the construction of a steerable basis for Gabor filters. We also demonstrate the generality of the new algorithm by applying it to to an example from structure from motion without missing data.
A new approach to 3-D terrain mapping

We discuss the problem of building larger high-resolution three-dimensional representations of unstructured terrain using terrestrial range sensors, which operate at the scale of meters to hundreds of meters. Issues specific to this sensing modality include widely varying resolution, absence of reliably detectable features, and very large data sets. We have developed a map building algorithm that registers and integrates sequences of range images, and we demonstrate its capabilities by building large terrain maps (260/spl times/166 meters) using ground-based and low-altitude terrestrial range sensors.
Virtual reality mapping system for Chernobyl accident site assessment

Initiated by the Department of Energy's International Nuclear Safety Program, an effort in underway to deliver and employ a telerobotic diagnostic system for structural evaluation and monitoring within the Chornobyl Unit-4 shelter. A mobile robot, named Pioneer, will enter the damaged Chornobyl structure and employ devices to measure radiation, temperature and humidity; acquire core samples of concrete structures for subsequent engineering analysis; and make photo-realistic 3D maps of the building interior. This paper details the latter element, dubbed 'C-Map', the Chornobyl Mapping System. C-Map consists of an automated 3D modeling system using stereo computer vision along with an interactive, virtual reality software program to acquire and analyze the photo-realistic 3D maps of the damaged building interior.
3D cueing: a data filter for object recognition

Presents a method for quickly filtering range data points to make object recognition in large 3D data sets feasible. The general approach, called "3D cueing", uses shape signatures from object models as the basis for a fast, probabilistic classification system which rates scene points in terms of their likelihood of belonging to a model. This algorithm which could be used as a front-end for any traditional 3D matching technique, is demonstrated using several models and cluttered scenes in which the model occupies between 1% and 50% of the data points.
ARTISAN: An Integrated Scene Mapping and Object Recognition System

Integration of three-dimensional textured scene mapping and object recognition presents many opportunities for assisted automation. We present Artisan, a software package that synthesizes these elements to form a user-friendly whole. Artisan uses a variety of 3D sensors, including laser range scanners and stereo systems, to acquire both image and range data. Artisan automatically finds the transformations between data taken at multiple sensor viewpoints using matching algorithms. The data from these viewpoints are then merged together to form an integrated textured map of the entire scene. Other user or sensor input can also inserted into the scene. Using object recognition with an expandable library of objects, Artisan can identify and locate simple and complex scene features. With this identity and transformation information, it is able to support many operations, including semi-automatic robotic teleoperation and navigation. After mapping and recognition, the identity, position, and orientation of the objects in the scene can be automatically transferred from the Artisan system into other software, including robotic teleoperation packages. Numerous opportunities for automation exist during the operations stage as a result of this increased world knowledge. This work was performed under contracts DE-AR26-97FT34314 and DE-AC21-92MC29104 for the Department of Energy, Federal Energy Technology Center, Morgantown, West Virginia.
Eecient Recovery of Low-dimensional Structure from High-dimensional Data

Many modeling tasks in computer vision. e.g. structure from motion, shape/reeectance from shading , lter synthesis have a low-dimensional intrinsic structure even though the dimension of the input data can be relatively large. We propose a simple but surprisingly eeective iterative randomized algorithm that drastically cuts down the time required for recovering the intrinsic structure. The computational cost depends only on the intrinsic dimension of the structure of the task. It is based on the recently proposed Cascade Basis Reduction (CBR) algorithm that was developed in the context of steerable lters. A key feature of our algorithm compared with CBR is that an arbitrary apriori basis for the task is not required. This allows us to extend the applicability of the algorithm to tasks beyond steerable lters such as structure from motion. We prove the convergence for the new algorithm. In practice the new algorithm is much faster than CBR for the same modeling error. We demonstrate this speed-up for the construction of a steerable basis for Gabor lters. We also demonstrate the generality of the new algorithm by applying it to to an example from structure from motion without missing data.
Mixed-Mode Control of Navigation for a Lunar Rover

The question of how to navigate is critically important to the success of any lunar rover mission. While humans typically have good judgement about which routes to take, they often get fatigued and disoriented when teleoperating rovers with time delay. On the other hand, while autonomous systems can produce safe and reliable navigation, they tend to be myopic. We are investigating mixed-mode methods of control that combine the strengths of humans and rovers. The rover uses range maps produced by stereo vision and a detailed model of the vehicle to evaluate the traversability of various paths. The evaluations are combined with recommendations from a human operator to produce a commanded steering angle and speed that is both safe and responsive to the operator's objectives. We have implemented and are testing such a system, using a prototype lunar rover that operates in outdoor, natural terrain.
Experiments with driving modes for urban robots

In this paper, we describe experiments on semi-autonomous control of a small urban robot. Three driving modes allow semi-autonomous control of the robot through video imagery, or by using partial maps of the environment. Performance is analyzed in terms of maximum speed, terrain roughness, environmental conditions, and ease of control. We concentrate the discussion on a driving mode based on visual servoing. In this mode, a template designated in an image is tracked as the robot moves toward the destination designated by the operator. Particular attention is given to the robustness of the tracking with respect to template selection, computational resources, occlusions, and rough motion. The discussion of algorithm performance is based on experiments conducted at Ft. Sam Houston, TX, on Jul. 5-9 1999. In addition to the driving modes themselves, the performance and practicality of an omnidirectional imaging sensor is discussed. In particular, we discuss the typical imaging artifacts due to ambient lighting.
Experimental analysis of Harmonic Shape Images

In our early work, a representation called Harmonic Shape Images for 3D free-form surfaces was proposed and applied to solve the surface matching problem (D. Zhang and M. Hebert, 1999). Extensive experiments using real data are conducted to analyze the performance of Harmonic Shape Images with respect to discriminability, stability and robustness to resolution and occlusion. The results show that Harmonic Shape Images are an appropriate representation for 3D surface comparison. Examples of surface comparison using real data are presented.
Robotic Rock Classification

This report describes a three-month research program undertook jointly by the Robotics Institute at Carnegie Mellon University and Ames Research Center as part of the Ames' Joint Research Initiative (JRI.) The work was conducted at the Ames Research Center by Mr. Liam Pedersen, a graduate student in the CMU Ph.D. program in Robotics under the supervision Dr. Ted Roush at the Space Science Division of the Ames Research Center from May 15 1999 to August 15, 1999. Dr. Martial Hebert is Mr. Pedersen's research adviser at CMU and is Principal Investigator of this Grant. The goal of this project is to investigate and implement methods suitable for a robotic rover to autonomously identify rocks and minerals in its vicinity, and to statistically characterize the local geological environment. Although primary sensors for these tasks are a reflection spectrometer and color camera, the goal is to create a framework under which data from multiple sensors, and multiple readings on the same object, can be combined in a principled manner. Furthermore, it is envisioned that knowledge of the local area, either a priori or gathered by the robot, will be used to improve classification accuracy. The key results obtained during this project are: The continuation of the development of a rock classifier; development of theoretical statistical methods; development of methods for evaluating and selecting sensors; and experimentation with data mining techniques on the Ames spectral library. The results of this work are being applied at CMU, in particular in the context of the Winter 99 Antarctica expedition in which the classification techniques will be used on the Nomad robot. Conversely, the software developed based on those techniques will continue to be made available to NASA Ames and the data collected from the Nomad experiments will also be made available.