Optimal Global Path Planning in Time Varying Environments Based on a Cost Evaluation Function

Gupta, Om K.; Jarvis, Ray

doi:10.1007/978-3-540-89378-3_15

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Similarly in [19], multi-steps of predicted OGMs are stacked in an XYT space (T stands for time) which is then used for path planning of rescue robots. The XYT stack of predicted OGMs is also used in other works, such as [9], however, the OGMs are either updated using object models (classic approach) or the main occupant are of the same object type, e.g. human in [17] and [19].…”

Section: Related Workmentioning

confidence: 99%

Multi-Step Prediction of Occupancy Grid Maps With Recurrent Neural Networks

Mohajerin

Rohani

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that accurate multi-step prediction of the drivable space can efficiently improve path planning and navigation resulting in safe, comfortable and optimum paths in autonomous driving. We train a variety of Recurrent Neural Network (RNN) based architectures on the OGM sequences from the KITTI dataset. The results demonstrate significant improvement of the prediction accuracy using our proposed difference learning method, incorporating motion related features, over the state of the art. We remove the egomotion from the OGM sequences by transforming them into a common frame. Although in the transformed sequences the KITTI dataset is heavily biased toward static objects, by learning the difference between consecutive OGMs, our proposed method provides accurate prediction over both the static and moving objects. A video of the performance of our method on the KITTI dataset is available at https: //youtu.be/Bskd0Z7eLFE.

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