Temporal accumulation of oriented visual features

In this paper we present a framework for accumulating on-line a model of a moving object (e.g., when manipulated by a robot). The proposed scheme is based on Bayesian filtering of local features, filtering jointly position, orientation and appearance information. The work presented here is novel in two aspects: first, we use an estimation mechanism that updates iteratively not only geometrical information, but also appearance information. Second, we propose a probabilistic version of the classical n-scan criterion that allows us to select which features are preserved and which are discarded, while making use of the available uncertainty model. The accumulated representations have been used in three different contexts: pose estimation, robotic grasping, and driver assistance scenario

Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that — in addition to commonly used geometric information — makes use of a novel multi–modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints

Model-Free 3D Shape Control of Deformable Objects Using Novel Features Based on Modal Analysis

Shape control of deformable objects is a challenging and important robotic problem. This paper proposes a model-free controller using novel 3D global deformation features based on modal analysis. Unlike most existing controllers using geometric features, our controller employs a physically-based deformation feature by decoupling 3D global deformation into low-frequency mode shapes. Although modal analysis is widely adopted in computer vision and simulation, it has not been used in robotic deformation control. We develop a new model-free framework for modal-based deformation control under robot manipulation. Physical interpretation of mode shapes enables us to formulate an analytical deformation Jacobian matrix mapping the robot manipulation onto changes of the modal features. In the Jacobian matrix, unknown geometry and physical properties of the object are treated as low-dimensional modal parameters which can be used to linearly parameterize the closed-loop system. Thus, an adaptive controller with proven stability can be designed to deform the object while online estimating the modal parameters. Simulations and experiments are conducted using linear, planar, and solid objects under different settings. The results not only confirm the superior performance of our controller but also demonstrate its advantages over the baseline method.Comment: Accepted by the IEEE Transactions on Robotics. The paper will appear in the IEEE Transactions on Robotics. IEEE copyrigh

Using Surfaces and Surface Relations in an Early Cognitive Vision System

The final publication is available at Springer via http://dx.doi.org/10.1007/s00138-015-0705-yWe present a deep hierarchical visual system with two parallel hierarchies for edge and surface information. In the two hierarchies, complementary visual information is represented on different levels of granularity together with the associated uncertainties and confidences. At all levels, geometric and appearance information is coded explicitly in 2D and 3D allowing to access this information separately and to link between the different levels. We demonstrate the advantages of such hierarchies in three applications covering grasping, viewpoint independent object representation, and pose estimation.European Community’s Seventh Framework Programme FP7/IC

TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning

To achieve accurate and low-cost 3D object detection, existing methods propose to benefit camera-based multi-view detectors with spatial cues provided by the LiDAR modality, e.g., dense depth supervision and bird-eye-view (BEV) feature distillation. However, they directly conduct point-to-point mimicking from LiDAR to camera, which neglects the inner-geometry of foreground targets and suffers from the modal gap between 2D-3D features. In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV. First, we introduce an inner-depth supervision module to learn the low-level relative depth relations between different foreground pixels. This enables the camera-based detector to better understand the object-wise spatial structures. Second, we design an inner-feature BEV distillation module to imitate the high-level semantics of different keypoints within foreground targets. To further alleviate the BEV feature gap between two modalities, we adopt both inter-channel and inter-keypoint distillation for feature-similarity modeling. With our target inner-geometry distillation, TiG-BEV can effectively boost BEVDepth by +2.3% NDS and +2.4% mAP, along with BEVDet by +9.1% NDS and +10.3% mAP on nuScenes val set. Code will be available at https://github.com/ADLab3Ds/TiG-BEV.Comment: Code link: https://github.com/ADLab3Ds/TiG-BE

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic