PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments

By Ruben Gomez-Ojeda, Jesús Briales, Javier González Jiménez. 2016

135 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, 3D Vision & Pose

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. Yin Zhou
2. Ruben Gomez-Ojeda
3. Oncel Tuzel
4. Francisco Angel Moreno
5. J. Gonzalez-Jimenez
6. David Zuñiga-Noël
7. Davide Scaramuzza
8. Jiaya Jia

In Computer Vision and Pattern Recognition

1. Yin Zhou
2. Oncel Tuzel
3. Ruben Gomez-Ojeda
4. Francisco Angel Moreno
5. J. Gonzalez-Jimenez
6. David Zuñiga-Noël
7. Xiaogang Wang
8. Davide Scaramuzza

In 3D Vision & Pose

1. Yin Zhou
2. Oncel Tuzel
3. Davide Scaramuzza
4. Jiaya Jia
5. Qi Chen
6. Shaoshuai Shi
7. Shu Liu
8. Charles Ruizhongtai Qi

Citations in the corpus

• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017, background): “Point cloud based 3D object detection is an important component of a variety of real-world applications, such as autonomous navigation [11, 14], housekeeping robots [28], and augmented/virtual reality [29].”
• PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, background): “[35] R. Gomez-Ojeda, J. Briales, and J. González-Jiménez, “PL-SVO: Semidirect monocular visual odometry by combining points and line segments,” in Proc.”