Performance analysis of single-query 6-DoF camera pose estimation in self-driving setups

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00334761" target="_blank" >RIV/68407700:21230/19:00334761 - isvavai.cz</a>

Result on the web

<a href="http://hdl.handle.net/10467/85606" target="_blank" >http://hdl.handle.net/10467/85606</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cviu.2019.04.009" target="_blank" >10.1016/j.cviu.2019.04.009</a>

Result language

angličtina

Original language name

Performance analysis of single-query 6-DoF camera pose estimation in self-driving setups

Original language description

In this work, we consider the problem of single-query 6-DoF camera pose estimation, i.e. estimating the position and orientation of a camera by using reference images and a point cloud. We perform a systematic comparison of three state-of-the-art strategies for 6-DoF camera pose estimation: feature-based, photometric-based and mutual-information-based approaches. Two standard datasets with self-driving setups are used for experiments, and the performance of the studied methods is evaluated in terms of success rate, translation error and maximum orientation error. Building on the analysis of the results, we evaluate a hybrid approach that combines feature-based and mutual-information-based pose estimation methods to benefit from their complementary properties for pose estimation. Experiments show that (1) in cases with large appearance change between query and reference, the hybrid approach outperforms feature-based and mutual-information-based approaches by an average increment of 9.4% and 8.7% in the success rate, respectively; (2) in cases where query and reference images are captured at similar imaging conditions, the hybrid approach performs similarly as the feature-based approach, but outperforms both photometric-based and mutual-informationbased approaches with a clear margin; (3) the feature-based approach is consistently more accurate than mutual-information-based and photometric-based approaches when at least 4 consistent matching points are found between the query and reference images.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-05360S" target="_blank" >GA18-05360S: Solving inverse problems for the analysis of fast moving objects</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Name of the periodical

Computer Vision and Image Understanding

ISSN

1077-3142

e-ISSN

1090-235X

Volume of the periodical

186

Issue of the periodical within the volume

Septamber

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

58-73

UT code for WoS article

000481564600006

EID of the result in the Scopus database

2-s2.0-85067195521