Geometric Perception Of Pose And Tracking

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Object Recognition and Full Pose Registration from a Single

perception is a must. In particular, an object recognition pose estimation and tracking geometric modeling to estimate the 6-DOF pose of objects.

Space-Time Localization and Mapping

allow for effective surface or feature tracking. Related work on the problem of geometry change de-tection from sparse imagery was investigated by [34] and [36], who detect geometric changes relative to an existing model using voxel-based appearance consistency to drive model updates. Change detection with viewability and oc-2

Visual Servoing and Robust Object Manipulation Using

Abstract Object tracking and manipulation is an important process for many applications in robotics and computer vision. A novel 3D pose estimation of objects using reectionally symmetry formulated in Conformal Geometric Algebra (CGA) is proposed in this work. The synthesis of the kinematics model for robots and a sliding mode controller using

Object Recognition and Pose Estimation using Color

and pose estimation based on color cooccurrence histograms and geometric model based techniques is presented. The particular problems addressed are: i) robust recognition of objects in natural scenes, ii) estimation of partial pose using an appearance based approach, and iii) complete 6DOF model based pose estimation and tracking.

Paper Number: I1 - Facebook Research

textures pose significant performance challenges [1]. Head mounted devices (HMD) use pose tracking to deliver the perception of presence. Tracking a user s head orientation in real time allows for dynamically adjusting images displayed on screen in front of each eye as to mimic the stereoscopic view that would be observed in reality.

Joint Detection, Tracking and Mapping by Semantic Bundle

vironment. The system builds on established tracking and mapping techniques to exploit incremental 3D reconstruc-tion in order to validate hypotheses on the presence and pose of sought objects. Then, detected objects are explic-itly taken into account for a global semantic optimization of both camera and object poses. Thus, unlike all systems

Marker-less Articulated Surgical Tool Detection

Surgical tool tracking can range from 2D patch tracking (e.g., identifying and following a 2D bounding box around the tool in a video sequence) [2] to 3D pose tracking (e.g., recovering the full 6-DOFs of rigid motion) [3] [4]. Tool tracking approaches typically divide into two categories: us-

Visual Recognition and Tracking for Perceptive Interfaces

Activity Independent Tracking Pose sensitive embedding provides fast approximation to Mobile devices should use perception to help Geometric Blur [Berg et al

Shape Recognition and Pose Estimation for Mobile Augmented

recognition, geometric projective invariance, 3D pose estimation, vision-based tracking, free-hand sketching, shape dual perception. INDEX TERMS: H.5.1 [Multimedia Information Systems]: Artificial, augmented, and virtual realities; I.4.0 [Image Processing and Computer Vision]: Scene Analysis Tracking 1 INTRODUCTION

Saliency Detection and Model-based Tracking: a Two Part

Model-based object tracking for localization has also been shown to be a powerful tool in aerial robots. Kemp12 uses a 3D model of the environment, projects that onto the image and optimizes the pose parameters through a likelihood function of the SSD distances from sampled points on the projected edges to nearby edges. One key to robustness during

SIGNet: Semantic Instance Aided Unsupervised 3D Geometry

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous sys- 49, 50, 11, 46], tracking, and pose es

Articulated Robot Motion for Simultaneous Localization and

turn used to estimate the pose of the depth sensor using mainly geometric techniques, such as point-to-plane ICP. Fully dense methods enable very high quality pose estimation and scene reconstruction within a small area, but they tend to drift over time, and are unable to track the sensor against scenes without much geometric structure.

MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM

estimate geometric, semantic, and motion properties for ar-bitrary objects in the scene. For each incoming frame, we perform instance segmentation to detect objects and refine mask boundaries using geometric and motion information. Meanwhile, we estimate the pose of each existing moving object using an object-oriented tracking method and

Tracking Objects with Point Clouds from Vision and Touch

order update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot s end effector. I. INTRODUCTION

visual slam - College of Computing

PTAM: Parallel Tracking and Mapping for Small AR Workspaces Tracking and Mapping are in two parallel threads. Tracking is frame-to-model, against the point clouds in the world. The global point clouds are initialized with epipolar geometry. Once we found feature points in key frames are not in the global frame, add them into the

Vision-based 3D Bicycle Tracking using Deformable Part Model

bicycle tracking framework for intelligent vehicles based on a detection method exploiting a deformable part model and a tracking method using an Interacting Multiple Model (IMM) algorithm. Bicycle tracking is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision

Novel Perception Algorithmic Framework For Object

perception algorithm one that requires no training for identifying & tracking objects in an autonomous vehicle s field of view. According to the methodology, by using the pose-estimation information of an ego vehicle, the PCL data from a 3D scanner (stereo camera or LiDAR) is transformed into a world coordinate system. Then, a KD-Tree

Autonomous Flight for Detection, Localization, and Tracking

of a small quadrotor, enabling tracking of a moving object. The 15 cm diameter, 250 g robot relies only on onboard sensors (a single camera and an inertial measurement unit) and computers, and can detect, localize, and track moving objects. Our key contributions include the relative pose esti-mate of a spherical target as well as the planning

Robust Visual Odometry to Irregular Illumination Changes with

pose estimation process: so called feature-based methods [2] and direct methods [3]. The feature-based methods encode an image to a list of keypoints (i.e. a list of image coordinates of distinctive points) and solve the geometric pose estimation problem on that list of coordinates and association table. Many keypoint

Efficiency in Real-time Webcam Gaze Tracking

1 Vicarious Perception Technologies [VicarVision], Amsterdam, The Netherlands 2 Delft University of Technology [TU Delft], Delft, The Netherlands famogh,[email protected], [email protected] Abstract. E ciency and ease of use are essential for practical appli-cations of camera based eye/gaze-tracking. Gaze tracking involves esti-

GeoNet: Unsupervised Learning of Dense Depth, Optical Flow

cal flow learning, but took no advantage of geometric con-sistency among predictions. By contrast, Godard et al. [15] exploited such constraints in monocular depth estimation by introducing a left-right consistency loss. However, they treat all the pixels equally, which would affect the effec-tiveness of geometric consistency loss in occluded

A comparison of geometric- and regression-based mobile gaze

A comparison of geometric- and regression-based mobile gaze-tracking. Björn Browatzki. 1, Heinrich H. Bülthoff. 1,2 * and Lewis L. Chuang. 1 * 1. Department of Perception, Cognition and Action

R. Craig Conlter CMU-RI-TR-92-01

pursuit path tracking algorithm. Given the general success of the algorithm over the past few years, it seems likely that it will be used again in land-based navigation problems. This report also includes a geometric derivation of the method, and presents some insights into the performance of the algorithm as a function of its parameters.


The core of EVO consists of two interleaved tracking and mapping modules (blocks marked with dashed lines in Fig. 2), thus following the separation principle of SLAM systems, such as PTAM [13]. The tracking module estimates the 6-DOF pose of the event camera using the event stream, assuming that a semi-dense 3D map of the environment is given. The

Visual Inertial Odometry with Pentafocal Geometric Constraints

pose a sliding-window visual inertial odometry using the pentafocal geometric constraints (combination of bifocal and trifocal tensors) between five images as the camera observation model. Furthermore, the pentafocal geomet-ric constraints are also chosen as the update model in the 1-point RANSAC algorithm [15] to perform robust mo-

Model Based Vehicle Tracking in Urban Environments

cle tracking literature, we utilize a model based approach, which uses RBPFs and eliminates the need for separate data Fig. 4. Dynamic Bayesian network model of the tracked vehicle pose X t, forward velocity v t, geometry G, and measurements Z t. segmentation and association stages. Our approach estimates position, velocity and shape of tracked

Facial Expression Recognition Based on Facial Action Unit

interest in the perception of human expressions and mental states by machines, and Facial Expression Recognition (FER) has attracted increasing attention. Facial Action Unit (AU) is an early proposed method to describe facial muscle movements, which can effectively reflect the changes in people s facial expressions.

Self-supervised Visual Descriptor Learning for Dense

of their tracking approach is not nearly as ne as one can achieve with a strong geometric model-based tracking system. Furthermore, because they have such a vast source of training data, they do not focus on re-visitation, so we do not know if Fig. 2: Example frames from both datasets. Note the view-point, lighting and pose variation.

RGB-D Object Tracking: A Particle Filter Approach on GPU

contour tracking approach. Since then, a large number of variants have been applied to the problem of body pose esti-mation [12], 3D object tracking [13], [14], [15], SLAM [16], etc. However, adopting the particle filtering approaches to robotic perception has been limited mainly due to their high computational cost. To tackle this problem

3D Pose Estimation of Daily Objects Using an RGB-D Camera

Object recognition and 6-DOF pose estimation are im-portant tasks in robotic perception. For the last decade, stable keypoint descriptors [1], [2] have led to successful progress on object recognition. As these keypoint descriptors are invariant to changes in illumination and geometric trans-formation, keypoint correspondences over different images

Event-based, 6-DOF Camera Tracking from Photometric Depth Maps

perform pose tracking as well as depth and intensity estimation. The system is computationally intensive, requiring a GPU for real-time operation. The parallel tracking-and-mapping system in [9] follows a geometric, semi-dense approach. The pose tracker is based on edge-map alignment and the scene depth is estimated

Semantic-Only Visual Odometry Based on Dense Class-Level

performance as geometric VO, the authors believe there is still room for improving dense geometric VO with the repre-sentational power of deep networks. The work most closely related to the present paper is by Czarnowski et al. [23]. This work also aims at developing a dense visual tracking approach based on a CNN image representation.

Efficient Rasterization for dge-Based 3D Object Tracking on

Analysis Tracking; Keywords: augmented reality, rasterization, pose tracking 1 Introduction Augmented reality modifies the perception of reality by embedding computer-generated information into images or videos. Because real-time operation is the key to provide an optimal user experience,

RGB-D Unidirectional Tracking

coarse pose tracking generates the initial camera pose using 3D correspondences with frame-by-frame registration. The initial camera poses are then used as inputs for the geometric integration model,

Fast model-based contact patch and pose estimation for highly

geometric information that enables the estimation and mea-surement of contact geometry and locations, forces, slip, and other properties essential for precise manipulation. Observing contact geometry allows the tactile sensing problem to be formulated as a 3D-perception problem, for which a wide range of proven techniques exist. However, key

Portable 3-D Modeling using Visual Pose Tracking

35 addition, visual pose tracking becomes inherently calibrated and synchronized with further image-based sensing. Visual pose tracking is a hard problem because geometric information be-comes entangled in radiometric and perspective geometric issues. Following distinct regions of interest in the images in realtime (feature-based tracking)

Real-Time Pose Estimation of Model-Based Rigid Object

Recognition Node : Givne the geometric model (w/ or o/ textures), extract descriptors (SIFT) then find a most confidential pose in codebook. Tracking Node : Given the initial pose from Recognition Node along with geometric model, utilize edge-basedmonte carlo particlefilter to track the rigid object. VIDEO STREAM MESH MODEL DATABASE

Robotic Learning of Manipulation Tasks from Visual Perception

hand, even if the problem of object tracking during the task perception is performed perfectly, retrieving full six-dimensional poses or trajectories of the scene objects from video images requires additional task knowledge, such as geometric models of the objects or the environment.

Perceiving, Learning, and Recognizing 3D Objects: An Approach

object based on a Kalman filter using geometric information as well as colour data (Oliveira et al. 2014). Object Tracking receives the point cloud of the detected object and computes the principal axes, an oriented bounding box for that point cloud. The pose of centre of bounding box of the object is then considered as the pose of the object.

Direction-Aware Semi-Dense SLAM

future perception systems will incorporate forms of scene understanding. In a step towards fully integrated prob-abilistic geometric scene understanding, localization and mapping we propose the first direction-aware semi-dense SLAM system. It jointly infers the directional Stata Center World (SCW) segmentation and a surfel-based semi-dense