Pose
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Pose refers to the position and orientation of an object, robot, or body part in 3D space, typically described by six degrees of freedom (6-DoF): three translational and three rotational parameters. In robotics and AI, pose estimation is the computational process of determining this spatial configuration from sensor data such as RGB images, depth maps, LiDAR point clouds, or IMU readings. It appears across a wide range of applications, including object manipulation (estimating a graspable object's 6D pose), robot localization (tracking where a robot is in its environment), visual odometry (inferring motion from camera frames), human-robot interaction (estimating body or hand joint configurations), and augmented reality. Techniques range from classical geometric methods and feature descriptors to modern deep learning approaches such as convolutional neural networks that directly regress pose parameters or iteratively refine estimates. Accurate pose estimation is foundational to autonomous systems because almost every downstream task—grasping, navigation, planning, or collision avoidance—depends on knowing precisely where things are and how they are oriented in the world.
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A benchmark for the evaluation of RGB-D SLAM systems
Jrgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, Daniel Cremers
Citations: 3918 • 2012
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Xiang Yu, Tanner Schmidt, Venkatraman Narayanan, Dieter Fox
Citations: 2088 • 2018
Visual Odometry [Tutorial]
Davide Scaramuzza, Friedrich Fraundorfer
Citations: 1485 • 2011
Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
Stefan Hinterstoißer, Vincent Lepetit, Slobodan Ilić, Stefan M. Holzer, Gary Bradski, Kurt Konolige, Nassir Navab
Citations: 1190 • 2013
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, Silvio Savarese
Citations: 1121 • 2019
Fast 3D recognition and pose using the Viewpoint Feature Histogram
Radu Bogdan Rusu, Gary Bradski, R. Thibaux, JJ Hsu
Citations: 859 • 2010
State Estimation for Robotics
Timothy D. Barfoot
Citations: 734 • 2017
Relative end-effector control using Cartesian position based visual servoing
W.J. Wilson, Carol Hulls, Gregory Bell
Citations: 621 • 1996
Speeded up detection of squared fiducial markers
Francisco J. Romero-Ramírez, Rafael Muñoz‐Salinas, R. Medina-Carnicer
Citations: 608 • 2018
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Yi Li, Gu Wang, Xiangyang Ji, Xiang Yu, Dieter Fox
Citations: 581 • 2018
The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM
Citations: 562 • 2017
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
Vincent Lepetit, Pascal Fua
Citations: 531 • 2005
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Guillermo Garcia-Hernando, Shanxin Yuan, Seungryul Baek, Tae‐Kyun Kim
Citations: 511 • 2018
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge
Andy Zeng, Kuan‐Ting Yu, Shuran Song, Daniel Suo, Ed Walker, Alberto Rodríguez, Jianxiong Xiao
Citations: 487 • 2017
The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection
Mihai Zanfir, Marius Leordeanu, Cristian Sminchisescu
Citations: 451 • 2013
The MOPED framework: Object recognition and pose estimation for manipulation
Alvaro Collet, Manuel Martínez, Siddhartha S Srinivasa
Citations: 443 • 2011
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
Citations: 439 • 2020
KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way
Ignacio Vizzo, Tiziano Guadagnino, Benedikt Mersch, Louis Wiesmann, Jens Behley, Cyrill Stachniss
Citations: 435 • 2023
Robot pose estimation in unknown environments by matching 2D range scans
Feng Lu, Evangelos Milios
Citations: 412 • 1994
Understanding the Limitations of CNN-Based Absolute Camera Pose Regression
Torsten Sattler, Qunjie Zhou, Marc Pollefeys, Laura Leal-Taixé
Citations: 401 • 2019