Visual SLAM algorithms: a survey from 2010 to 2016

By Takafumi Taketomi, Hideaki Uchiyama, Sei Ikeda. IPSJ Transactions on Computer Vision and Applications, 2017

749 citations recorded by the source corpus.

SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8a043ca4a868a7f1099da1c8ef5928295969d338

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

1. Gordon Wetzstein
2. Bokui (William) Shen
3. Gwenolé Corre
4. Frédérick Carrel
5. Andréa Macario Barros
6. Adam W. Harley
7. Maugan Michel
8. Leonidas J. Guibas

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Andrea Vedaldi
3. Jitendra Malik
4. Gordon Wetzstein
5. Leonidas J. Guibas
6. Ming-Hsuan Yang
7. Trevor Darrell
8. Adam W. Harley

In Electrical and Electronic Engineering

1. Leonidas J. Guibas

Citations in the corpus

• A Comprehensive Survey of Visual SLAM Algorithms (2022, methodology): “RGB-D systems present advantages such as providing color image data and dense depth map without any pre-processing step, hence decreasing the complexity of the SLAM initialization [10].”
• PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking (2023, background): “Early motion estimation methods cast point tracking as an optimization problem defined on handcrafted features [27, 37, 57, 52, 12], and these techniques continue to drive structure-from-motion [11, 31, 43] and simultaneous localization and mapping systems [54].”