다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

Theory of fuzzy integrals and its applications

This paper presents a planning method based on mapping moving obstacles into C-space for safe interaction between human arms and robot manipulators. In pre-processing phase, a hybrid distance metric is defined to select neighboring sampled nodes in C-space to construct a roadmap. Then, two kinds of mapping are constructed to determine invalid and dangerous edges in the roadmap for each basic cell decomposed in workspace. For updating the roadmap when an obstacle is moving, basic cells covering the obstacle's surfaces are mapped into the roadmap by using new positions of the surfaces points sampled on the obstacle. In query phase, in order to predict and avoid coming collisions and reach the goal efficiently, an interaction strategy with six kinds of planning actions of searching, updating, walking, waiting, dodging and pausing are designed. Simulated experiments show that the proposed method is efficient for safe interaction between two working robot manipulators and two randomly moving human arms.

A planning method for safe interaction between human arms and robot manipulators

This paper presents a path planner based on PRM framework for robots operating in changing environments, in which obstacles can move randomly and robots may change their original shapes, e.g. a robot manipulator grasps an object. W-C nodes mapping coupled with lazy edges evaluation is used to ensure a generated path containing only valid nodes and edges when constructed probabilistic roadmap becomes partially invalid in changing environments. Our method combines merits of DRM and Lazy PRM methods. W-C nodes mapping, which is constructed in pre-processing phase by mapping every basic cell in workspace to nodes of roadmap, is preserved to indicate invalid nodes of roadmap fast whenever obstacles move. W-C edges mapping, which is another mapping relationship of DRM method, is skipped since it is much more complicated and time-consuming for construction. The simplified mapping with smaller size can be recomputed or modified fast when robots change their original shapes. Instead, lazy edges evaluation is used to ensure all edges valid along a found path. Simulated experiments for a dual-manipulator system show that our method is efficient for path planning in changing environments

A Path Planner in Changing Environments by Using W-C Nodes Mapping Coupled with Lazy Edges Evaluation

Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visualinertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problems. The intrawindow optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source1.

/pdf/unsupervised-monocular-visual-inertial-odometry-network-1po85o1kwg.pdf

Unsupervised Monocular Visual-inertial Odometry Network

Obtaining reliable and discriminative target representation are two vital tasks for data association in multi-tracking. Pervious works always directly combine bunch of features for more discriminative target representation, but this is prone to error accumulation and unnecessary computational cost, which on the contrary may increase identity switches in data association. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which have been settled in complex and various scenes, previous fixed feature selection scheme cannot meet general requirements. To address this problem, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features which have higher reliability on target representation in applied scene, and gradually combines features to the minimum requirements of discriminating ambiguous targets. Hierarchical feature space is constructed according to reliability of features in the multi-tracking system, and data association is conducted in different layers of the feature space adaptively. Our algorithm is validated on various challenging RGB-D and RGB datasets recorded in various indoor and outdoor scenes, for diversities of both features and scenes. Experimental results validate its effectiveness and efficiency.

Scene-Adaptive Hierarchical Data Association for Multiple Objects Tracking

This paper presents an efficient path planner to identify difficult regions for path planning in changing environments, in which obstacles can move randomly. The difficult regions consist of narrow passages and the boundaries of obstacles in robot Configuration Space (C-space). These regions exert significantly negative influence on finding a valid path in static environments. The problem becomes more complicated in changing environments, because that the regions will change their positions when obstacles move. Besides, it is necessary to identify difficult regions in real time since obstacles may move frequently. To identify difficult regions fast when they change their positions, a dynamic bridge builder is proposed based on a W-C nodes mapping and a Bridge planner method. The W-C nodes mapping is used not only to conserve the validity of nodes in C-space, but also to provide the information about where a "bridge" should be built, i.e. the positions of narrow passages, and where the boundaries of obstacles are. Furthermore, a hierarchy sampling strategy is employed to boost the density of nodes in difficult regions efficiently. In the query phase, a Lazy-edges evaluation method is adopted to validate the edges in a found path. Simulated experiments for a dual-manipulator system show that our method is efficient for path planning in changing environments.

Hong Liu

Papers

A planning method for safe interaction between human arms and robot manipulators

A Path Planner in Changing Environments by Using W-C Nodes Mapping Coupled with Lazy Edges Evaluation

Unsupervised Monocular Visual-inertial Odometry Network

Scene-Adaptive Hierarchical Data Association for Multiple Objects Tracking

A dynamic bridge builder to identify difficult regions for path planning in changing environments