Paper Info
Title
2-Entity Ransac For Robust Visual Localization In Changing Environment
Abstract
Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes across weathers and seasons, as there is significant appearance variation between the map and the query image. The crucial challenge in this situation is that the percentage of outliers, i.e. incorrect feature matches, is high. In this paper, we derive minimal closed form solutions for 3D-2D localization with the aid of inertial measurements, using only 2 point matches or 1 point match and 1 line match. These solutions are further utilized in the proposed 2-entity RANSAC, which is more robust to outliers as both line and point features can be used simultaneously and the number of matches required for pose calculation is reduced. Furthermore, we introduce three feature sampling strategies with different advantages, enabling an automatic selection mechanism. With the mechanism, our 2-entity RANSAC can be adaptive to the environments with different distribution of feature types in different segments. Finally, we evaluate the method on both synthetic and real-world datasets, validating its performance and effectiveness in inter-session scenarios.
Year
DOI
Venue
2019
10.1109/IROS40897.2019.8967671
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
Volume
Inertial frame of reference,Computer vision,RANSAC,Visual localization,Outlier,Control engineering,Artificial intelligence,Sampling (statistics),Engineering,Robot
Journal
abs/1903.03967
ISSN
Citations 
PageRank 
2153-0858
1
0.35
References 
Authors
16
7
Name
Order
Citations
PageRank
Yanmei Jiao153.11
Yue Wang2960143.63
Bo Fu310.69
Xiaqing Ding4397.49
Qimeng Tan512.38
Lei Chen66239395.84
Rong Xiong77722.86