arXiv 2209.057222023-05-16cs.RO

GrASPE：用于非结构化室外环境中机器人导航的基于图的多模态融合

GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Jing Liang, Tianrui Guan, Utsav Patel, Dinesh Manocha

AI总结：

提出一种基于图多模态融合的轨迹可通行性估计与规划算法，利用GNN融合多传感器特征并量化其可靠性，在复杂室外环境中显著提升了导航成功率并降低了误报估计。

摘要：

我们提出了一种用于复杂室外环境中机器人导航的新型轨迹可通行性估计与规划算法。我们结合来自RGB相机、3D LiDAR和机器人里程计传感器的多模态感官输入，训练预测模型，基于部分可靠的多模态传感器观测来估计候选轨迹的成功概率。我们使用编码器网络将高维多模态感官输入编码为低维特征向量，并将其表示为连通图。然后利用该图训练基于注意力的图神经网络（GNN）来预测轨迹成功概率。我们进一步分别分析了图像中的特征数量（角点）和点云数据中的特征数量（边缘和平面），以量化其可靠性，从而增强GNN中使用的特征图表示的权重。在运行期间，我们的模型利用多传感器输入来预测局部规划器生成的轨迹的成功概率，以避免潜在碰撞和失败。当一个或多个传感器模态在复杂室外环境中不可靠或不可用时，我们的算法展现出稳健的预测能力。我们使用Spot机器人在真实室外环境中评估了算法的导航性能。与最先进的导航方法相比，我们观察到导航成功率提高了10-30%，误报估计减少了13-15%。

英文摘要：

We present a novel trajectory traversability estimation and planning algorithm for robot navigation in complex outdoor environments. We incorporate multimodal sensory inputs from an RGB camera, 3D LiDAR, and the robot's odometry sensor to train a prediction model to estimate candidate trajectories' success probabilities based on partially reliable multi-modal sensor observations. We encode high-dimensional multi-modal sensory inputs to low-dimensional feature vectors using encoder networks and represent them as a connected graph. The graph is then used to train an attention-based Graph Neural Network (GNN) to predict trajectory success probabilities. We further analyze the number of features in the image (corners) and point cloud data (edges and planes) separately to quantify their reliability to augment the weights of the feature graph representation used in our GNN. During runtime, our model utilizes multi-sensor inputs to predict the success probabilities of the trajectories generated by a local planner to avoid potential collisions and failures. Our algorithm demonstrates robust predictions when one or more sensor modalities are unreliable or unavailable in complex outdoor environments. We evaluate our algorithm's navigation performance using a Spot robot in real-world outdoor environments. We observe an increase of 10-30% in terms of navigation success rate and a 13-15% decrease in false positive estimations compared to the state-of-the-art navigation methods.

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