Igor Gilitschenski

2.7k total citations
86 papers, 1.6k citations indexed

About

Igor Gilitschenski is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Igor Gilitschenski has authored 86 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 33 papers in Computer Vision and Pattern Recognition and 29 papers in Aerospace Engineering. Recurrent topics in Igor Gilitschenski's work include Target Tracking and Data Fusion in Sensor Networks (24 papers), Robotics and Sensor-Based Localization (24 papers) and Autonomous Vehicle Technology and Safety (17 papers). Igor Gilitschenski is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (24 papers), Robotics and Sensor-Based Localization (24 papers) and Autonomous Vehicle Technology and Safety (17 papers). Igor Gilitschenski collaborates with scholars based in United States, Switzerland and Germany. Igor Gilitschenski's co-authors include Uwe D. Hanebeck, Roland Siegwart, Gerhard Kurz, Daniela Rus, Juan Nieto, Sertaç Karaman, Marcin Dymczyk, Simon Julier, Marius Fehr and Alexander Amini and has published in prestigious journals such as The International Journal of Robotics Research, IEEE Transactions on Intelligent Transportation Systems and Journal of Statistical Software.

In The Last Decade

Igor Gilitschenski

81 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Igor Gilitschenski United States 22 695 688 486 307 222 86 1.6k
Jesse Levinson United States 9 1.0k 1.4× 799 1.2× 246 0.5× 742 2.4× 333 1.5× 9 1.9k
Denis F. Wolf Brazil 20 811 1.2× 434 0.6× 192 0.4× 610 2.0× 182 0.8× 101 1.6k
Oliver Pink Germany 8 680 1.0× 458 0.7× 212 0.4× 670 2.2× 240 1.1× 10 1.5k
Chieh‐Chih Wang Taiwan 17 957 1.4× 1.0k 1.5× 339 0.7× 291 0.9× 409 1.8× 64 1.6k
Hans‐Joachim Wuensche Germany 20 908 1.3× 798 1.2× 192 0.4× 675 2.2× 176 0.8× 99 1.7k
Dirk Langer United States 11 674 1.0× 365 0.5× 221 0.5× 569 1.9× 223 1.0× 23 1.4k
Seiichi Mita Japan 21 1.1k 1.5× 416 0.6× 180 0.4× 668 2.2× 206 0.9× 124 1.7k
Alex Teichman United States 10 639 0.9× 316 0.5× 229 0.5× 481 1.6× 190 0.9× 11 1.3k
David Stavens United States 8 686 1.0× 441 0.6× 227 0.5× 529 1.7× 386 1.7× 9 1.5k
Shunsuke Kamijo Japan 24 615 0.9× 936 1.4× 291 0.6× 429 1.4× 773 3.5× 149 2.0k

Countries citing papers authored by Igor Gilitschenski

Since Specialization
Citations

This map shows the geographic impact of Igor Gilitschenski'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 Igor Gilitschenski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Igor Gilitschenski more than expected).

Fields of papers citing papers by Igor Gilitschenski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Igor Gilitschenski. 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 Igor Gilitschenski. The network helps show where Igor Gilitschenski may publish in the future.

Co-authorship network of co-authors of Igor Gilitschenski

This figure shows the co-authorship network connecting the top 25 collaborators of Igor Gilitschenski. A scholar is included among the top collaborators of Igor Gilitschenski 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 Igor Gilitschenski. Igor Gilitschenski 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.
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3.
Siarohin, Aliaksandr, et al.. (2023). iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis. 1–12. 5 indexed citations
4.
Li, Xiao, Igor Gilitschenski, Guy Rosman, Sertaç Karaman, & Daniela Rus. (2023). Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving. IEEE Robotics and Automation Letters. 8(8). 4737–4744.
5.
Walls, Jeffrey M., et al.. (2022). MapLite 2.0: Online HD Map Inference Using a Prior SD Map. IEEE Robotics and Automation Letters. 7(3). 8355–8362. 10 indexed citations
6.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2021). Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features. IEEE Robotics and Automation Letters. 6(2). 3459–3466. 40 indexed citations
7.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2021). Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN). IEEE Robotics and Automation Letters. 7(2). 984–991. 5 indexed citations
8.
Amini, Alexander, Igor Gilitschenski, J. M. Phillips, et al.. (2020). Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation. IEEE Robotics and Automation Letters. 5(2). 1143–1150. 140 indexed citations
9.
Gilitschenski, Igor, et al.. (2020). Autonomous Navigation in Inclement Weather Based on a Localizing Ground Penetrating Radar. IEEE Robotics and Automation Letters. 5(2). 3267–3274. 38 indexed citations
10.
Gilitschenski, Igor, et al.. (2020). Deep Context Maps: Agent Trajectory Prediction Using Location-Specific Latent Maps. IEEE Robotics and Automation Letters. 5(4). 5097–5104. 10 indexed citations
11.
Banerjee, Rohan, et al.. (2019). MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map. IEEE Robotics and Automation Letters. 5(2). 556–563. 15 indexed citations
12.
McGill, Stephen G., Guy Rosman, Alyssa Pierson, et al.. (2019). Probabilistic Risk Metrics for Navigating Occluded Intersections. IEEE Robotics and Automation Letters. 4(4). 4322–4329. 24 indexed citations
13.
Schneider, Thomas, Marcin Dymczyk, Marius Fehr, et al.. (2018). Maplab: An Open Framework for Research in Visual-Inertial Mapping and Localization. IEEE Robotics and Automation Letters. 3(3). 1418–1425. 175 indexed citations
14.
Mielenz, Holger, et al.. (2018). Inferring Pedestrian Motions at Urban Crosswalks. IEEE Transactions on Intelligent Transportation Systems. 20(2). 544–555. 47 indexed citations
15.
Dubé, Renaud, Hannes Sommer, Igor Gilitschenski, et al.. (2018). Incremental-Segment-Based Localization in 3-D Point Clouds. IEEE Robotics and Automation Letters. 3(3). 1832–1839. 48 indexed citations
16.
Gilitschenski, Igor, Gerhard Kurz, Uwe D. Hanebeck, & Roland Siegwart. (2016). Optimal quantization of circular distributions. Repository KITopen (Karlsruhe Institute of Technology). 1813–1820. 4 indexed citations
17.
Kurz, Gerhard, Igor Gilitschenski, & Uwe D. Hanebeck. (2014). Deterministic approximation of circular densities with symmetric Dirac mixtures based on two circular moments. KITopen. 1–8. 11 indexed citations
18.
Gilitschenski, Igor, Gerhard Kurz, Simon Julier, & Uwe D. Hanebeck. (2014). A new probability distribution for simultaneous representation of uncertain position and orientation. Repository KITopen (Karlsruhe Institute of Technology). 1–7. 21 indexed citations
19.
Gilitschenski, Igor, Gerhard Kurz, & Uwe D. Hanebeck. (2013). Bearings-only sensor scheduling using circular statistics. International Conference on Information Fusion. 515–521. 7 indexed citations
20.
Gilitschenski, Igor & Uwe D. Hanebeck. (2012). A robust computational test for overlap of two arbitrary-dimensional ellipsoids in fault-detection of Kalman filters. International Conference on Information Fusion. 396–401. 6 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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