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I am Jesus Briales, a researcher in the Machine Perception and Intelligent Robotics group (MAPIR) at the University of Málaga (Spain). My research interests lie in the area of computer vision and include:

    • Visual odometry and SLAM.
    • Sensor Fusion.
    • Optimization.
    • Algebraic minimal solutions.

I received my Bacherlor's and Master's degree in Industrial Engineering (a general engineering which covers mechanics, electricity, electronic, computer science, etc.) from the University of Malaga in 2014. In October 2013 I joined the MAPIR group as a Researcher in Computer Vision.

Research (from most recent to oldest)

Global optimality of solutions in SLAM:

State-of-the-art approaches for SLAM take a graph-based approach that seeks the best model from the consensus of many available measurements from different devices. This problem becomes hard due to both the inherent non-linearities in the problem as well as the scalability issues with map size. As a result, we end up with a high-dimensional non-linear problem that is addressed through local iterative optimization techniques.

Lagrangian duality makes it possible to find the global optimal solution for PGO problems where the strong duality condition is fulfilled. When facing more challenging PGO problems this condition is not satisfied and we resort to iterative methods where the initialization is vital. In "Initialization of 3D Pose Graph Optimization using Lagrangian duality" we exploit our novel formulation for 3D PGO (in the work below) to provide an initial guess that outperforms state-of-the-art initialization approaches. If there is strong duality, our method directly delivers the global solution but at a much lower cost than existing approaches.

SLAM has become a fundamental tool for many modern applications. So, in real applications, we need to detect wrong solutions in order to avoid catastrophic failure. In "Fast Global Optimality Verification in 3D SLAM" we propose a novel formulation of the SLAM problem and exploit it to verify optimality much faster than the state-of-the-art method.

Line-based monocular odometry:

Most state-of-the-art odometry and SLAM pipelines use point features only, so their behavior may degrade in low-textured scenarios. Human-made scenarios are structured and provide a lot of information as line segments.

The introduction of line segments in traditional odometry pipelines rendered previous proposals notably slow. In "PL-SVO: Semi-Direct Monocular Visual Odometry by Combining Points and Line Segments" we propose an extension of a successful semi-direct approach, SVO, that exploits both point and line segment features. It works in a wider variety of scenarios and, very important, it keeps real-time performance.

Manhattan scene with camera for JMIV Image of Manhattan scene

In "A minimal closed-form solution for the Perspective Three orthogonal Angles (P3oA) problem. Application to visual odometry" we provide a simple closed-form solution to the Perspective Three orthogonal Angles (P3oA) problem: given the projection of three orthogonal lines in a calibrated camera, find their 3D directions. Upon this solution, an algorithm for the estimation of the camera relative rotation between two frames is proposed.

Extrinsic sensor calibration:

Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine measurements from both sensors a precise estimation of their relative pose (or extrinsic calibration) is required.

We provide a new minimal solution for the extrinsic calibration of a 2D Laser-Rangefinder and a Camera which reduces the calibration task to the mere observation of a trihedron. The corresponding method outperforms the state-of-the-art alternatives in robustness. When an iterative method is used to get a fine calibration, our proposal provides a reliable initial estimate thus benefitting the overall calibration process.

Scene with camera and LRF for ICRA15

A novel approach is proposed for the calibration of a Camera-LRF rig using all the geometric data gathered from a set of observations of an orthogonal trihedron. The problem is solved in a probabilistic framework, so that data uncertainty is taken into account. As a result, our method provides higher robustness and precision than the state-of-the-art methods.



Jesus Briales Garcia
Dpto. Ingenieria de Sistemas y Automatica
E.T.S.I. Informatica - Telecomunicacion
Universidad de Malaga

Campus Universitario de Teatinos
29071 Malaga, Spain
e-mail: jbriales [at]