Visual SLAM

Visual SLAM scheme Visual SLAM consist in building a map of an unknown environment while tracking its position simultaneously using the partially built map.
Nowadays, is one of the most challenging open problems for developing truly autonomous robots. Our research focuses on the development of robust and efficient methods to perform visual SLAM (Simultaneous Localization and Mapping) with different type of sensors (i.e. monocular, stereo, RGB-D and 2D LRFs) in real time.


PL-SLAM:  We propose a combined approach to stereo visual SLAM based on the simultaneous employment of both point and line segment features, as in our previous approaches to Visual Odometry, that is capable of working robustly in a wide variety of scenarios. As a consequence, we also obtain meaningful maps that can be further employed to extract valuable information from structured scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features, with an ad-hoc implementation of the combined algorithm to solve the bundle adjustment problem in this particular case.

Arxiv draft:

Code: (coming soon)


A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual SLAM  
In this work we proposed a novel approach called Sparser Relative Bundle Adjustment (SRBA), which exploits the inherent flexibility of the relative BA (RBA) framework to devise a continuum of strategies, ranging from RBA with linear graphs to classic BA in global coordinates, where submapping with local maps emerges as a natural intermediate solution. This method leads to graphs that can be optimized in bounded-time even at loop closures, regardless of the loop length. Furthermore, it is shown that the pattern in which relative coordinate variables are defined among keyframes has a significant impact on the graph optimization problem. By using the proposed scheme, optimization can be done more efficiently than in standard RBA, allowing the optimization of larger local maps for any given maximum computational cost.

The main algorithms involved in the graph management, along with their complexity analyses, are presented to prove their bounded-time nature. One key advance of the present work is the demonstration that, under mild assumptions, the spanning trees for every single keyframe in the map can be incrementally built by a constant-time algorithm, even for arbitrary graph topologies. We validate our proposal within the scope of visual stereo SLAM by developing a complete system that includes a front-end that seamlessly integrates several state-of-the-art computer vision techniques such as ORB features and bag-of-words, along with a decision scheme for keyframe insertion and a SRBA-based back-end that operates as graph optimizer.

Link to the article

Code available in GitHub (in development)


Please refer to the following articles for further details:

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