A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

Corte, Bartolomeo Della; Bogoslavskyi, Igor; Stachniss, Cyrill; Grisetti, Giorgio

doi:10.1109/icra.2018.8461049

Cited by 18 publications

(16 citation statements)

References 9 publications

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“…The approaches maintain a sparse feature map. Without occupancy grid or dense point cloud outputs available out‐of‐the‐box like lidar approaches, they can be then difficult to use on a real robot. Dense visual odometry (DVO)‐SLAM (Kerl, Sturm, & Cremers, ), Reg Green Blue inverse Depth (RGBiD)‐SLAM (Gutierrez‐Gomez, Mayol‐Cuevas, & Guerrero, ), and multi‐cue photometric registration (MPR) (Della Corte, Bogoslavskyi, Stachniss, & Grisetti, ), instead of using local visual features to estimate motion, use photometric and depth errors over all pixels of the RGB‐D images. They can generate dense point clouds of the environment.…”

Section: Popular Slam Approaches Available On Rosmentioning

confidence: 99%

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Labbé

Michaud

2018

Journal of Field Robotics

717

325

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Distributed as an open-source library since 2013, real-time appearance-based mapping (RTAB-Map) started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of constraints on sensors, processing capabilities, and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power, and ease of integration. Since most of SLAM approaches are either visual-or lidar-based, comparison is difficult. Therefore, we decided to extend RTAB-Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB-Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real-world datasets (e.g., KITTI, EuRoC, TUM RGB-D, MIT Stata Center on PR2 robot), outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications. K E Y W O R D S perception, position estimation, SLAM

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Section: Popular Slam Approaches Available On Rosmentioning

confidence: 99%

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Labbé

Michaud

2018

Journal of Field Robotics

717

325

View full text Add to dashboard Cite

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“…Such initialization is usually obtained by estimating the transformation between two images using a combination of RANSAC and direct solvers, and then computing the depth through triangulation between the stereo pair. Della Corte et al [31] developed a registration algorithm called MPR, which was built on this idea. As a result, MPR is able to operate on depth images capturing different cues and obtained with arbitrary projection functions.…”

Section: Ils In Roboticsmentioning

confidence: 99%

“…In this case, the block size is determined from the dimension of the perturbation vector of the variables in the template argument list. We extended the factors at this level to implement approaches such as dense multi-cue registration [31]. Special structures in the Jacobians can be exploited to speed up the calculation of H k whose computation has a non negligible cost.…”

Section: Factorsmentioning

confidence: 99%

Least Squares Optimization: From Theory to Practice

et al. 2020

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Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system that addresses problems transparently with a different structure and designed to be easy to extend. The system is written in modern C++ and runs efficiently on embedded systemsWe validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios.

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“…Della Corte et. al [31] developed a registration algorithm called MPR, which was built on this idea. As a result, MPR is able to operate on depth images capturing different cues and obtained with arbitrary projection functions.…”

Section: A Ils In Roboticsmentioning

confidence: 99%

Least Squares Optimization: from Theory to Practice

Grisetti¹,

Guadagnino²,

Aloise³

et al. 2020

Preprint

Self Cite

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Nowadays, Non-Linear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system, that addresses transparently problems with a different structure and designed to be easy to extend. The system is written in modern C++ and can run efficiently on embedded systems 3 . We validated our approach by conducting comparative experiments on several problems using tandard datasets. The results show that our system achieves state-ofthe-art performances in all tested scenarios.

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A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

Cited by 18 publications

References 9 publications

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Least Squares Optimization: From Theory to Practice

Least Squares Optimization: from Theory to Practice

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