Nonlinear factor recovery for long-term SLAM

Mazuran, Mladen; Burgard, Wolfram; Tipaldi, Gian Diego

doi:10.1177/0278364915581629

Cited by 49 publications

(78 citation statements)

References 38 publications

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“…Optionally, this cropped problem can be solved using (2). Then, the new solution can be used henceforth, yielding slightly better results especially in on-line cases [12]. The selected node is marginalized via Schur complement, generating a dense information matrix Λ, as in fig.…”

Section: Iinode Removal and Sparsification In Graph Slammentioning

confidence: 99%

“…This is sometimes called a subgraph (SG) topology, as in [12]. Alternatively, departing from the CLT again, the cliquey topology [12] converts pairs of independent factors into one single factor by correlating them. In order to gather density, while SG adds more sparse factors, a cliquey topology densifies the existing ones.…”

Section: A Topologymentioning

confidence: 99%

“…To obtain the projection, one can re-parametrize the problem to relative poses w.r.t an arbitrarily chosen node [10,11] or use a rank-revealing eigen decomposition [12].…”

Section: B Factor Recovery Through Kld Minimizationmentioning

confidence: 99%

“…Different approaches pose sparsification as a KLD minimization problem [10]- [12]. The best sparse approximation is the one with a minimum KLD with respect to the dense distribution resulting from marginalization.…”

Section: Introductionmentioning

confidence: 99%

“…State of the art methods [10]- [12] propose the use of interior point and projected quasi-Newton optimization methods to achieve sparsification. Both methods require the tuning of a set of parameters that strongly affects their convergence and robustness.…”

Section: Introductionmentioning

confidence: 99%

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Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Vallvé

Solà

Andrade‐Cetto

2018

IEEE Robot. Autom. Lett.

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Abstract-Current solutions to the simultaneous localization and mapping (SLAM) problem approach it as the optimization of a graph of geometric constraints. Scalability is achieved by reducing the size of the graph, usually in two phases. First, some selected nodes in the graph are marginalized and then, the dense and non-relinearizable result is sparsified. The sparsified network has a new set of relinearizable factors and is an approximation to the original dense one. Sparsification is typically approached as a Kullback-Liebler divergence (KLD) minimization between the dense marginalization result and the new set of factors. For a simple topology of the new factors, such as a tree, there is a closed form optimal solution. However, more populated topologies can achieve a much better approximation because more information can be encoded, although in that case iterative optimization is needed to solve the KLD minimization. Iterative optimization methods proposed by the state-of-art sparsification require parameter tuning which strongly affect their convergence. In this paper, we propose factor descent and non-cyclic factor descent, two simple algorithms for SLAM sparsification that match the state-of-art methods without any parameters to be tuned. The proposed methods are compared against the state of the art with regards to accuracy and CPU time, in both synthetic and real world datasets.

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Section: Iinode Removal and Sparsification In Graph Slammentioning

confidence: 99%

Section: A Topologymentioning

confidence: 99%

“…To obtain the projection, one can re-parametrize the problem to relative poses w.r.t an arbitrarily chosen node [10,11] or use a rank-revealing eigen decomposition [12].…”

Section: B Factor Recovery Through Kld Minimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Vallvé

Solà

Andrade‐Cetto

2018

IEEE Robot. Autom. Lett.

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Experimental evaluation of Visual‐Inertial Odometry systems for arable farming

Cremona

Comelli

Pire

2022

Journal of Field Robotics

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The farming industry constantly seeks the automation of different processes involved in agricultural production, such as sowing, harvesting and weed control. The use of mobile autonomous robots to perform those tasks is of great interest. Arable lands present hard challenges for Simultaneous Localization and Mapping (SLAM) systems, key for mobile robotics, given the visual difficulty due to the highly repetitive scene and the crop leaves movement caused by the wind. In recent years, several Visual-Inertial Odometry (VIO) and SLAM systems have been developed.They have proved to be robust and capable of achieving high accuracy in indoor and outdoor urban environments. However, they were not properly assessed in agricultural fields. In this study we assess the most relevant state-of-the-art VIO systems in terms of accuracy and processing time on arable lands to better understand how they behave on these environments. In particular, the evaluation is carried out on a collection of sensor data recorded by our wheeled robot in a soybean field, which was publicly released as the Rosario data set. The evaluation shows that the highly repetitive appearance of the environment, the strong vibration produced by the rough terrain and the movement of the leaves caused by the wind, expose the limitations of the current state-of-the-art VIO and SLAM systems. We analyze the systems failures and highlight the observed drawbacks, including initialization failures, tracking loss and sensitivity to Inertial Measurement Unit saturation. Finally, we conclude that even though certain systems like ORB-SLAM3 and stereo Multi-State Constraint Kalman Filter show good results with respect to others, more improvements should be done to make them reliable in agricultural fields for certain applications such as soil tillage of crop rows and pesticide spraying.

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A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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Nonlinear factor recovery for long-term SLAM

Cited by 49 publications

References 38 publications

Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Graph SLAM Sparsification With Populated Topologies Using Factor Descent Optimization

Experimental evaluation of Visual‐Inertial Odometry systems for arable farming

A systematic literature review on long‐term localization and mapping for mobile robots

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