Local Motion Planning for Ground Mobile Robots via Deep Imitation Learning

Saleh, Khaled J.; Attia, Mohamed; Hossny, Mohammed; Hanoun, Samer; Salaken, Syed Moshfeq; Nahavandi, Saeid

doi:10.1109/smc.2018.00691

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Recently, deep learning-based techniques such as convolution neural networks (ConvNets) have been achieving stateof-the-art results in many computer vision tasks such: object identification [1], scene understanding [2], [3], and human action recognition [4]- [6]. However, these techniques require a handful amount of labelled data for training them which is both time-consuming and cumbersome to get for many tasks.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data

Saleh

Abobakr

Attia

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7% in average precision score over other baseline approaches when tested on real BEV point cloud images.

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