Skip to Main content Skip to Navigation
Conference papers

Parsimonious real time monocular SLAM

Abstract : This paper presents a real time monocular EKF SLAM process that uses only Cartesian defined landmarks. This representation is easy to handle , light and consequently fast. However , it is prone to linearization errors which can cause the filter to diverge. Here , we will first clearly identify and explain when those problems take place. Then , a solution , able to reduce or avoid the errors involved by the linearization process , will be proposed. Combined with an EKF , our method uses resources parsimoniously by conserving landmarks for a long period of time without requiring many points to be efficient. Our solution is based on a method to properly compute the projection of a 3D uncertainty into the image frame in order to track landmarks efficiently. The second part of this solution relies on a correction of the Kalman gain that reduces the impact of the update when it is incoherent. This approach was applied to a real data set presenting difficult conditions such as severe distortions , reflections , blur or sunshine to illustrate its robustness. I. INTRODUCTION In order to be autonomous , a vehicle must be able to localize itself in an unknown environment. This problem , known as the Simultaneous Localization And Mapping (SLAM) problem , has been extensively studied throughout the last two decades [ 11 ]. Though mathematical solutions have been proposed [ 10 ] , they are usually not sufficient in real world applications. Moreover , they mostly consider range and bearing sensors [ 2 ]. Laser Range Finders (LRFs) , for instance , are expensive and rarely furnish enough information to efficiently track a feature. These two constraints are essential and must be avoided in order to develop and spread the use of Intelligent Vehicles. Conversely to LRFs , cameras are cheap and rich in terms of information. However , they add other constraints. First , cameras are bearing-only sensors which means that the depth of a landmark cannot be accurately estimated without having several observations with a sufficient parallax. Then , environmental factors can degrade the image quality : blur while turning , distortions , reflections or sunshine. These effects appear a lot especially in urban environments which are information rich. This is why a system must be tested under difficult conditions in order to prove its capabilities in real life applications. To do so , it is necessary to take into account the way features are initialized and handled within the state vector. Indeed , with difficult conditions , it is obvious that landmarks will be discarded quickly. With a bearing-only sensor , it is an major issue as several observations of the same landmark are needed to have an accurate estimate of its position. Two main solutions exist in the literature : delayed and undelayed initializations. The first way is straightforward : it only uses well-defined landmarks [ 1 ] [ 9 ]. This means that no wrong
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download
Contributor : Guillaume Bresson Connect in order to contact the contributor
Submitted on : Wednesday, August 3, 2016 - 4:13:24 PM
Last modification on : Wednesday, April 21, 2021 - 8:52:05 AM


Files produced by the author(s)



Guillaume Bresson, Thomas Féraud, Romuald Aufrère, Paul Checchin, Roland Chapuis. Parsimonious real time monocular SLAM. IEEE International Conference on Intelligent Vehicles, 2012, Alcalá de Henares, Spain. ⟨10.1109/IVS.2012.6232203⟩. ⟨hal-01351399⟩



Record views


Files downloads