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Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map

2019-06-06 08:16:31
Huifang Ma, Yue Wang, Li Tang, Sarath Kodagoda, Rong Xiong

Abstract

Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications. The high demand for localization accuracy has been essential for safe robot planing and navigation while it makes the current geometric solutions less robust to environmental changes. Recent research on end-to-end methods handle raw sensory data with forms of navigation instructions and directly output the command for robot control. However, the lack of intermediate semantics makes the system more rigid and unstable for practical use. To explore these issues, this paper proposes an innovate navigation framework based on the GPS-level localization, which takes the raw perception data with publicly accessible navigation maps to produce an intermediate navigation cost map that allows subsequent flexible motion planning. A deterministic conditional adversarial network is adopted in our method to generate visual goal-directed paths under diverse navigation conditions. The adversarial loss avoids the pixel-level annotation and enables a weakly supervised training strategy to implicitly learn both of the traffic semantics in image perceptions and the planning intentions in navigation instructions. The navigation cost map is then rendered from the goal-directed path and the concurrently collected laser data, indicating the way towards the destination. Comprehensive experiments have been conducted with a real vehicle running in our campus and the results have verified the robustness to localization error of the proposed navigation system.

Abstract (translated)

基于精确定位的自主导航在学术研究和实际应用中得到了广泛的发展。对定位精度的高要求对于机器人的安全规划和导航至关重要,同时也使得现有的几何解对环境变化的鲁棒性降低。最近对端到端方法的研究以导航指令的形式处理原始感官数据,并直接输出机器人控制命令。然而,由于缺乏中间语义,使得系统在实际应用中更加僵化和不稳定。为了探讨这些问题,本文提出了一种基于GPS水平定位的创新导航框架,该框架利用原始感知数据和可公开获取的导航地图生成一个中间导航成本地图,从而实现后续的灵活运动规划。该方法采用确定性条件对抗网络,在不同的导航条件下生成视觉目标导向路径。对抗性损失避免了像素级注释,并使弱监督的训练策略能够隐式学习图像感知中的交通语义和导航指令中的规划意图。然后,根据目标导向路径和同时收集的激光数据绘制导航成本图,指示到达目的地的路径。以某实际车辆在我校进行了综合试验,结果证明了该导航系统对定位误差的鲁棒性。

URL

https://arxiv.org/abs/1906.02468

PDF

https://arxiv.org/pdf/1906.02468.pdf


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