Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning

Mann, Khushdeep Singh; Tomy, Abhishek; Paigwar, Anshul; Renzaglia, Alessandro; Laugier, Christian

doi:10.1109/iv51971.2022.9827412

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Their algorithm can perform continuous mapping of the occupancy status of dynamic environments while maintaining good accuracy for dynamic obstacle modeling. Mann et al (2022) proposed a spatio‐temporal learning‐based occupancy grids prediction approach in dynamic environments. This approach can predict the occupancy grids for a longer horizon of 3 s in dynamic environments.…”

Section: Mavs Onboard Obstacle Descriptionmentioning

confidence: 99%

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

Lü

Fasano

Carrio³

et al. 2023

Journal of Field Robotics

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In recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions.The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non-cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non-cooperative flying objects. In this article, we review the topic of non-cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non-cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

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Section: Mavs Onboard Obstacle Descriptionmentioning

confidence: 99%

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

Lü

Fasano

Carrio³

et al. 2023

Journal of Field Robotics

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“…We use N = 100 to ensure a fine sampling of the deviations. Our approach suits well with the probabilistic occupancy paradigm and achieves great real-time performances although it is not as accurate as stateof-art of occupancy prediction such as [20] or [21].…”

Section: B Occupancy Predictionmentioning

confidence: 99%

From Probabilistic Occupancy Grids to versatile Collision Avoidance using Predictive Collision Detection

Genevois,

Rummelhard,

Spalanzani

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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As robots are spreading in diverse environments, new collision avoidance challenges arise. Real world collision avoidance often includes dynamic, complex, unstructured and uncertain environments. While these constraints are rarely considered all together in perception and navigation research works, we present a broad integration from perception to navigation in a versatile collision avoidance approach that is designed to operate under these constraints. Our solution relies on a new Predictive Collision Detector that we propose as an interface between state-of-art grid-based perception and sampling-based planners. Unlike most other approaches, ours operates only on elementary spatial occupancy and does not use the concept of objects such that it captures the richness and versatility of modern occupancy grid perception. With simulated and experimental results on 2 robots, including a robotic car, we show that our solution generates advanced driving behaviors that compare with the related state-of-art and comply with safety standards even in complex scenarios. With this contribution, we hope to facilitate the use of probabilistic occupancy grids for collision avoidance and to enhance the connectivity of the state-of-art of perception and navigation.

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“…Previous works by Mann et al [21] and Asghar et al [22] present occupancy grid prediction models that use semantic labels of the occupied cells to improve prediction. Semantic labels consist of only vehicle information in both of these approaches, whereas our proposed SMGMs contain other semantic labels in addition to the vehicle labels (e.g., vehicles, cyclists, pedestrians).…”

Section: A Predicting Future Occupancy Statesmentioning

confidence: 99%

Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments

Toyungyernsub

Yel

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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For autonomous vehicles to proactively plan safe trajectories and make informed decisions, they must be able to predict the future occupancy states of the local environment. However, common issues with occupancy prediction include predictions where moving objects vanish or become blurred, particularly at longer time horizons. We propose an environment prediction framework that incorporates environment semantics for future occupancy prediction. Our method first semantically segments the environment and uses this information along with the occupancy information to predict the spatiotemporal evolution of the environment. We validate our approach on the real-world Waymo Open Dataset. Compared to baseline methods, our model has higher prediction accuracy and is capable of maintaining moving object appearances in the predictions for longer prediction time horizons.

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Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning

Cited by 8 publications

References 10 publications

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

From Probabilistic Occupancy Grids to versatile Collision Avoidance using Predictive Collision Detection

Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments

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