Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments

Senanayake, Ransalu; Toyungyernsub, Maneekwan; Wang, Mingyu; Kochenderfer, Mykel J.; Schwager, Mac

doi:10.1109/itsc45102.2020.9294288

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…A similar approach, called Directional grid map (DGM) is proposed by Senanayake et al (2020) and Senanayake and Ramos (2018). However, in contrast to the work of Kucner et al (2017), the authors use a von Mises distribution to model the direction of motion, and to each mode of this distribution the authors associate a β -distribution to model the speed.…”

Section: Surveymentioning

confidence: 99%

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Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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Section: Surveymentioning

confidence: 99%

“…Apart from well-established motion prediction algorithms, also other approaches have been developed over time. One such approach utilizing the flow field is the work by Senanayake et al (2020). In this work, the authors estimate the distribution of future agent states using a flow map.…”

Section: Applications Of Modsmentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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“…We can find also unique measurements of a map quality like the Pearson correlation coefficient (Ak et al, 2018), Cramer-von-Mises criterion (Bennetts et al, 2019), Kullback-Leibler divergence (Rudenko et al, 2020), and k-NN Universal Divergence Estimator (Kucner et al, 2017). Rarely, we can find average probability density (Senanayake and Ramos, 2018;Senanayake et al, 2020) and chi-square distance (Vintr et al, 2019a;Molina et al, 2019;Stuede and Schappler, 2022).…”

Section: Benchmarking Spatio-temporal Mapsmentioning

confidence: 99%

“…The model that estimates a human’s future position directly from observations ( Kollmitz et al, 2015 ) allows for reactive navigation that respects a human’s personal space without the need of repetitive recalculations of the robot’s trajectory. ( Senanayake et al, 2020 ) proposed a static spatial map that includes distributions of directions and speeds of the vehicles at the crossroads. It is used prior to the trajectory prediction, which is updated based on the current observation.…”

Section: Related Workmentioning

confidence: 99%

“…A very popular measure of the quality of discrete or discretized maps is mean square error ( Senanayake et al, 2016 ; Yan et al, 2017 ; Senanayake and Ramos, 2018 ; Krajník et al, 2019 ) and rooted mean square error ( Guizilini and Ramos, 2015 ; Jovan et al, 2016 ; Zhou and Matteson, 2016 ; Vintr et al, 2019a ; Senanayake et al, 2020 ). Apart from the fact that, in general, the maps not providing the frequency of measurements need to be normalized ( Ak et al, 2018 ), the difference between maps is very small ( Vintr et al, 2019a , 2020 ), which leads to the necessity to enhance the differences of results ( Vintr et al, 2019b ), and the rank of methods is dependent on the coarseness of the grid ( Kubiš, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

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Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

et al. 2022

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Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.

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