A Method for the Perceptual Optimization of Complex Visualizations

A common problem in visualization applications is the display of one surface overlying another. Unfortunately, it is extremely difficult to do this clearly and effectively. Stereoscopic viewing can help, but in order for us to be able to see both surfaces simultaneously, they must be textured, and the top surface must be made partially transparent. There is also abundant evidence that all textures are not equal in helping to reveal surface shape, but there are no general guidelines describing the best set of textures to be used in this way. What makes the problem difficult to perceptually optimize is that there are a great many variables involved. Both foreground and background textures must be specified in terms of their component colors, texture element shapes, distributions, and sizes. Also to be specified is the degree of transparency for the foreground texture components. Here we report on a novel approach to creating perceptually optimal solutions to complex visualization problems and we apply it to the overlapping surface problem as a test case. Our approach is a three-stage process. In the first stage we create a parameterized method for specifying a foreground and background pair of textures. In the second stage a genetic algorithm is applied to a population of texture pairs using subject judgments as a selection criterion. Over many trials effective texture pairs evolve. The third stage involves characterizing and generalizing the examples of effective textures. We detail this process and present some early results

A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation

Intrinsic isometric shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency, i.e., the metric structure of the whole manifold must not change significantly. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise (incomplete data and contacts), which is a common problem in real-world 3D scanner data. In this paper, we introduce a new approach to partial, intrinsic isometric matching. Our method is based on the observation that isometries are fully determined by purely local information: a map of a single point and its tangent space fixes an isometry for both global and the partial maps. From this idea, we develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent spaces. From this, we derive a local propagation algorithm that find such mappings efficiently. In contrast to previous heuristics based on RANSAC or expectation maximization, our method is based on a simple and sound theoretical model and fully deterministic. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure

Planetary cartography in the next decade: Digital cartography and emerging opportunities

Planetary maps being produced today will represent views of the solar system for many decades to come. The primary objective of the planetary cartography program is to produce the most complete and accurate maps from hundreds of thousands of planetary images in support of scientific studies and future missions. Here, the utilization of digital techniques and digital bases in response to recent advances in computer technology are emphasized

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Building colour terms: A combined GIS and stereo vision approach to identifying building pixels in images to determine appropriate colour terms

Color information is a useful attribute to include in a building’s description to assist the listener in identifying the intended target. Often this information is only available as image data, and not readily accessible for use in constructing referring expressions for verbal communication. The method presented uses a GIS building polygon layer in conjunction with street-level captured imagery to provide a method to automatically filter foreground objects and select pixels which correspond to building fac¸ades. These selected pixels are then used to define the most appropriate color term for the building, and corresponding fuzzy color term histogram. The technique uses a single camera capturing images at a high frame rate, with the baseline distance between frames calculated from a GPS speed log. The expected distance from the camera to the building is measured from the polygon layer and refined from the calculated depth map, after which building pixels are selected. In addition significant foreground planar surfaces between the known road edge and building fac¸ade are identified as possible boundarywalls and hedges. The output is a dataset of the most appropriate color terms for both the building and boundary walls. Initial trials demonstrate the usefulness of the technique in automatically capturing color terms for buildings in urban regions

Percepción Activa multi-robot para la cobertura rápida y eficiente de escenas.

The efficiency of path-planning in robot navigation is crucial in tasks, such as search-and-rescue and disaster surveying, but this is emphasised even more when considering multi-rotor aerial robots due to the limited battery and flight time. In this spirit, this Master Thesis proposes an efficient, hierarchical planner to achieve a comprehensive visual coverage of large-scale outdoor scenarios for small drones. Following an initial reconnaissance flight, a coarse map of the scene gets built in real-time. Then, regions of the map that were not appropriately observed are identified and grouped by a novel perception-aware clustering process that enables the generation of continuous trajectories (sweeps) to cover them efficiently. Thanks to this partitioning of the map in a set of tasks, we are able to generalize the planning to an arbitrary number of drones and perform a well-balanced workload distribution among them. We compare our approach to an alternative state-of-the-art method for exploration and show the advantages of our pipeline in terms of efficiency for obtaining coverage in large environments.<br /

Multi-robot active perception for fast andefficient scene reconstruction.

The efficiency of path-planning in robot navigation is crucial in tasks, such as search-and-rescue and disaster surveying, but this is emphasised even more when considering multi-rotor aerial robots due to the limited battery and flight time. In this spirit, this Master Thesis proposes an efficient, hierarchical planner to achieve a comprehensive visual coverage of large-scale outdoor scenarios for small drones. Following an initial reconnaissance flight, a coarse map of the scene gets built in real-time. Then, regions of the map that were not appropriately observed are identified and grouped by a novel perception-aware clustering process that enables the generation of continuous trajectories (sweeps) to cover them efficiently. Thanks to this partitioning of the map in a set of tasks, we are able to generalize the planning to an arbitrary number of drones and perform a well-balanced workload distribution among them. We compare our approach to an alternative state-of-the-art method for exploration and show the advantages of our pipeline in terms of efficiency for obtaining coverage in large environments.<br /

Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking

The most common paradigm for vision-based multi-object tracking is tracking-by-detection, due to the availability of reliable detectors for several important object categories such as cars and pedestrians. However, future mobile systems will need a capability to cope with rich human-made environments, in which obtaining detectors for every possible object category would be infeasible. In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects. We present an efficient segmentation mask-based tracker which associates pixel-precise masks reported by the segmentation. Our approach can utilize semantic information whenever it is available for classifying objects at the track level, while retaining the capability to track generic unknown objects in the absence of such information. We demonstrate experimentally that our approach achieves performance comparable to state-of-the-art tracking-by-detection methods for popular object categories such as cars and pedestrians. Additionally, we show that the proposed method can discover and robustly track a large variety of other objects.Comment: ICRA'18 submissio

NR-SLAM: Non-Rigid Monocular SLAM

In this paper we present NR-SLAM, a novel non-rigid monocular SLAM system founded on the combination of a Dynamic Deformation Graph with a Visco-Elastic deformation model. The former enables our system to represent the dynamics of the deforming environment as the camera explores, while the later allows us to model general deformations in a simple way. The presented system is able to automatically initialize and extend a map modeled by a sparse point cloud in deforming environments, that is refined with a sliding-window Deformable Bundle Adjustment. This map serves as base for the estimation of the camera motion and deformation and enables us to represent arbitrary surface topologies, overcoming the limitations of previous methods. To assess the performance of our system in challenging deforming scenarios, we evaluate it in several representative medical datasets. In our experiments, NR-SLAM outperforms previous deformable SLAM systems, achieving millimeter reconstruction accuracy and bringing automated medical intervention closer. For the benefit of the community, we make the source code public.Comment: 12 pages, 7 figures, submited to the IEEE Transactions on Robotics (T-RO