MonoSLAM: Real-Time Single Camera SLAM

By Andrew J. Davison, Ian David Reid, Nicholas David Molton, Olivier Stasse. 2007

4,323 citations recorded by the source corpus.

We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Continuous Scene Representations for Embodied AI (2022, background): “Classical mapping and localization work in the robotics literature [16, 19, 35] relies on lowlevel geometric features and does not encode semantic information.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “Which one is best: Feature-based or direct methods? Featurebased approaches are quite mature, with a long history of success [60].”
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration (2011, background): “For example, Davison et al. (2007) describe an extended Kalman filter (EKF)-based system that is able to localize a camera in a room-sized environment.”
1-Point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry (2010, methodology): “Other methods substitute stereovision by motion and use a single monocular camera (Davison et al., 2007; Holmes et al., 2008; Montiel et al., 2006).”
Fast Keypoint Recognition Using Random Ferns (2010, methodology): “18 shows only tiny jags in the order of a few millimeters and appears as smooth as a trajectory that was estimated in a filtered approach to SLAM, such as MonoSLAM [11], [10].”
Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, neutral): “…data and multi-view geometry to estimate a robot’s pose and construct low-level maps (e.g., sparse, semi-dense, or dense point clouds) through methods like ﬁlter-based approaches (e.g., MonoSLAM [58]), keyframe-based methods (e.g., ORB-SLAM [59]), and direct trackingtechniques(e.g.,LSD-SLAM[60]).”
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights (2012, background): “isual recognition of places and place sequences in the field of robotic navigation has advanced rapidly in recent years, with several techniques [1-5] now being competitive with laser range-based techniques.”
Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map (2017, background): “Getting accurate estimation of 6-DoF camera pose from an image is essential for many computer vision applications such as robot navigation [15, 37], augmented reality [35, 55], and image-based 3D reconstruction [4,18].”
Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking (2011, methodology): “Abbreviations: IMU = inertial measurement unit, SLAM = simultaneous localization and mapping b Abbreviations used in these columns will be explained in Sects.”
Visual SLAM: Why filter? (2012, positive): “An early approach was developed by Chiuso et al. [3], while Davison et al.'s MonoSLAM [9] [11] followed a similar method with developments such as active feature measurement and local loop closure.”
Elastic Fragments for Dense Scene Reconstruction (2013, background): “[16], building on work on range image integration [2], real-time range scanning [23], and monocular SLAM [3, 4, 12] showed that registering each input image to a growing volumetric model can average out high-frequency error and produce smooth reconstructions of objects and small scenes.”
Robust visual SLAM across seasons (2015, background): “A great amount of research has been dedicated to appearance-based mapping [8], [6], [7].”
Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments (2012, background): “While we have recently seen some very successful monocular VSLAM systems [10], [11], it is PTAM [12] that has been the most popular across the Robotics literature due to the free availability of the CONFIDENTIAL.”
Automatic loop closure detection using multiple cameras for 3D indoor localization (2012, background): “In addition, recent advances in visual odometry algorithms have led to camerabased SLAM approaches [14, 7].”
RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, background): “It is also widely used in the ﬁelds of computer vision [2], [3], robot vision [4], [5], medical image analysis [6] and so on.”
Direct Sparse Odometry (2016, background): “Note that in time this may well change with the introduction of more realistic, unbiased priors learnt from real-world data.”
Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition (2020, background): “Place recognition is the core technique of many important applications such as simultaneous localization and mapping (SLAM) [4] and autonomous navigation [5], [6].”