Monocular
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About
Monocular refers to vision systems that rely on a single camera to perceive and interpret the surrounding environment, as opposed to stereo or multi-camera setups. In robotics and AI, monocular approaches are used across a wide range of tasks including simultaneous localization and mapping (SLAM), visual odometry, object tracking, depth estimation, pedestrian detection, and autonomous navigation. Algorithms process the single image stream to recover 3D scene structure, estimate camera motion, detect obstacles, and localize a robot—all without the geometric baseline that stereo rigs provide. Because depth cannot be triangulated directly from one viewpoint, monocular systems rely on techniques such as structure-from-motion, motion parallax, learning-based depth prediction, and sensor fusion with inertial measurement units to overcome inherent scale ambiguity. Monocular systems matter because a single camera is lightweight, inexpensive, and power-efficient, making it ideal for platforms with strict payload or cost constraints such as micro aerial vehicles and mobile robots. Their widespread adoption has driven advances in deep learning and probabilistic estimation that benefit the broader computer vision and robotics communities.
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Top Cited Papers
MonoSLAM: Real-Time Single Camera SLAM
Andrew J. Davison, Ian Reid, Nicholas Molton, Olivier Stasse
Citations: 3909 • 2007
Monocular Pedestrian Detection: Survey and Experiments
Markus Enzweiler, Dariu M. Gavrila
Citations: 1226 • 2008
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Berta Bescos, José M. Fácil, Javier Civera, José Neira
Citations: 924 • 2018
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
Alessandro Giusti, Jérôme Guzzi, Dan Cireşan, Fang-Lin He, J. Rodriguez, Flavio Fontana, Matthias Faessler, Christian Förster, Jürgen Schmidhuber, Gianni A. Di, Davide Scaramuzza, Luca Maria Gambardella
Citations: 694 • 2015
Semi-dense Visual Odometry for a Monocular Camera
Jakob Engel, Jürgen Sturm, Daniel Cremers
Citations: 538 • 2013
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
Vincent Lepetit, Pascal Fua
Citations: 531 • 2005
Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Vincent Casser, Sören Pirk, Reza Mahjourian, Anelia Angelova
Citations: 491 • 2019
Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera
Fangchang Ma, Guilherme V. Cavalheiro, Sertaç Karaman
Citations: 471 • 2019
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
Vincent Lepetit, Pascal Fua
Citations: 469 • 2005
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment
Linhui Xiao, Jinge Wang, Xiaosong Qiu, Rong Zheng, Xudong Zou
Citations: 344 • 2019
Scale Drift-Aware Large Scale Monocular SLAM
Hauke Strasdat, J. M. M. Montiel, Andrew J. Davison
Citations: 340 • 2011
Monocular Vision for Mobile Robot Localization and Autonomous Navigation
Eric Royer, Maxime Lhuillier, Michel Dhome, Jean‐Marc Lavest
Citations: 324 • 2007
Scale Drift-Aware Large Scale Monocular SLAM
H. Strasdat, J. M. M. Montiel, A. Davison
Citations: 306 • 2010
Appearance-Based Obstacle Detection with Monocular Color Vision
Iwan Ulrich, Illah Nourbakhsh
Citations: 300 • 2000
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
Tong Qin, Jie Pan, Shaozu Cao, Shaojie Shen
Citations: 296 • 2019
Applications of dynamic monocular machine vision
Ernst D. Dickmanns, Volker Graefe
Citations: 280 • 1988
Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration
Zhenfei Yang, Shaojie Shen
Citations: 277 • 2016
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection
Sergey Levine, Peter Pástor, Alex Krizhevsky, Deirdre Quillen
Citations: 276 • 2017
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pástor, Alex Krizhevsky
Citations: 272 • 2017
Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation
Shreeyak S. Sajjan, Matthew R. Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, Shuran Song
Citations: 258 • 2020