Luis Montano

2.5k total citations
86 papers, 1.7k citations indexed

About

Luis Montano is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Luis Montano has authored 86 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Computer Vision and Pattern Recognition, 49 papers in Aerospace Engineering and 24 papers in Control and Systems Engineering. Recurrent topics in Luis Montano's work include Robotics and Sensor-Based Localization (49 papers), Robotic Path Planning Algorithms (43 papers) and Distributed Control Multi-Agent Systems (12 papers). Luis Montano is often cited by papers focused on Robotics and Sensor-Based Localization (49 papers), Robotic Path Planning Algorithms (43 papers) and Distributed Control Multi-Agent Systems (12 papers). Luis Montano collaborates with scholars based in Spain, Portugal and United States. Luis Montano's co-authors include Javier Mínguez, Luis Montesano, Luis Riazuelo, José Luis Villarroel, J. M. M. Montiel, Danilo Tardioli, José Santos-Victor, Pedro U. Lima, Luís Paulo Reis and António Paulo Moreira and has published in prestigious journals such as Sensors, Pattern Recognition and The International Journal of Robotics Research.

In The Last Decade

Luis Montano

84 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Luis Montano Spain 24 1.1k 882 494 428 204 86 1.7k
Libor Přeučil Czechia 22 953 0.9× 831 0.9× 331 0.7× 436 1.0× 278 1.4× 88 1.6k
Jan Faigl Czechia 26 1.2k 1.1× 964 1.1× 271 0.5× 461 1.1× 271 1.3× 137 2.1k
K. Madhava Krishna India 21 1.2k 1.1× 935 1.1× 337 0.7× 208 0.5× 134 0.7× 177 1.8k
Nicholas Gans United States 22 1.1k 0.9× 761 0.9× 688 1.4× 336 0.8× 192 0.9× 142 2.0k
Mina Kamel Switzerland 18 1.1k 1.0× 962 1.1× 625 1.3× 272 0.6× 183 0.9× 30 1.8k
Iwan Ulrich United States 10 1.4k 1.2× 954 1.1× 393 0.8× 190 0.4× 136 0.7× 11 2.0k
Anthony Stentz United States 21 927 0.8× 662 0.8× 365 0.7× 169 0.4× 204 1.0× 51 1.5k
Mário Sarcinelli-Filho Brazil 29 1.3k 1.2× 800 0.9× 1.2k 2.4× 784 1.8× 199 1.0× 237 2.8k
Lucia Pallottino Italy 21 799 0.7× 672 0.8× 729 1.5× 383 0.9× 216 1.1× 94 1.8k
Michael Suppa Germany 21 1.1k 1.0× 1.2k 1.4× 497 1.0× 174 0.4× 215 1.1× 71 1.9k

Countries citing papers authored by Luis Montano

Since Specialization
Citations

This map shows the geographic impact of Luis Montano's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Luis Montano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luis Montano more than expected).

Fields of papers citing papers by Luis Montano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luis Montano. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Luis Montano. The network helps show where Luis Montano may publish in the future.

Co-authorship network of co-authors of Luis Montano

This figure shows the co-authorship network connecting the top 25 collaborators of Luis Montano. A scholar is included among the top collaborators of Luis Montano based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Luis Montano. Luis Montano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Riazuelo, Luis, et al.. (2025). SHINE: Social homology identification for navigation in crowded environments. The International Journal of Robotics Research. 45(1). 60–79.
2.
Riazuelo, Luis, et al.. (2025). RUMOR: Reinforcement learning for understanding a model of the real world for navigation in dynamic environments. Robotics and Autonomous Systems. 191. 105020–105020. 1 indexed citations
3.
Montano, Luis, et al.. (2024). Occupation-aware planning method for robotic monitoring missions in dynamic environments. Robotics and Autonomous Systems. 185. 104892–104892. 1 indexed citations
4.
Placed, Julio A., et al.. (2024). From Underground Mines to Offices: A Versatile and Robust Framework for Range-Inertial SLAM. arXiv (Cornell University). 1–8. 1 indexed citations
5.
Placed, Julio A., et al.. (2024). G-Loc: Tightly-Coupled Graph Localization With Prior Topo-Metric Information. IEEE Robotics and Automation Letters. 9(11). 9167–9174. 3 indexed citations
6.
Riazuelo, Luis, et al.. (2023). Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS. Applied Sciences. 13(15). 8925–8925. 5 indexed citations
7.
Riazuelo, Luis, et al.. (2022). RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments. Sensors. 22(10). 3847–3847. 8 indexed citations
8.
Lázaro, María T., et al.. (2022). Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework. Sensors. 22(4). 1390–1390. 5 indexed citations
9.
Montano, Luis, et al.. (2019). Multi-robot coordination for connectivity recovery after unpredictable environment changes. IFAC-PapersOnLine. 52(8). 446–451. 2 indexed citations
10.
Concha, Alejo, Wajahat Hussain, Luis Montano, & Javier Civera. (2015). Incorporating scene priors to dense monocular mapping. Autonomous Robots. 39(3). 279–292. 12 indexed citations
11.
Montano, Luis, et al.. (2015). Muscular fatigue detection using sEMG in dynamic contractions. PubMed. 2015. 494–497. 7 indexed citations
12.
Concha, Alejo, Wajahat Hussain, Luis Montano, & Javier Civera. (2014). Manhattan and Piecewise-Planar Constraints for Dense Monocular Mapping. 19 indexed citations
13.
Montano, Luis, et al.. (2012). An optimized model for estimation of muscle contribution and human joint torques from sEMG information. PubMed. 2012. 3364–3367. 3 indexed citations
14.
Riazuelo, Luis, et al.. (2008). Cooperative navigation using environment compliant robot formations. IRIS Research product catalog (Sapienza University of Rome). 2789–2794. 28 indexed citations
15.
Montano, Luis, et al.. (2007). Comparative experiments on optimization criteria and algorithms for auction based multi-robot task allocation. Proceedings - IEEE International Conference on Robotics and Automation/Proceedings. 3345–3350. 24 indexed citations
16.
Mínguez, Javier & Luis Montano. (2007). Abstracting any Vehicle Shape and the Kinematics and Dynamic Constraints from Reactive Collision Avoidance Methods.. 1 indexed citations
17.
Montano, Luis, et al.. (2006). Environment Understanding: Robust Feature Extraction from Range Sensor Data. 3337–3343. 7 indexed citations
18.
Montesano, Luis, José Gaspar, José Santos-Victor, & Luis Montano. (2005). Cooperative localization by fusing vision-based bearing measurements and motion. 2333–2338. 27 indexed citations
19.
Mínguez, Javier & Luis Montano. (2004). The ego-kinodynamic space: collision avoidance for any shape mobile robots with kinematic and dynamic constraints. 1. 637–643. 7 indexed citations
20.
Montano, Luis, et al.. (2000). Using the Time Petri Net Formalism for Specification, Validation, and Code Generation in Robot-Control Applications. The International Journal of Robotics Research. 19(1). 59–76. 27 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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