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.

翻译：我们提出了一种新颖的轨迹可通行性估计与规划算法，用于复杂室外环境中的机器人导航。该算法融合RGB相机、3D LiDAR及机器人里程计传感器的多模态感知输入，训练预测模型以基于部分可靠的多模态传感器观测值估算候选轨迹的成功概率。通过编码器网络将高维多模态感知输入转化为低维特征向量，并将其表示为连通图，进而利用该图训练基于注意力机制的图神经网络（GNN）以预测轨迹成功概率。我们进一步分别分析图像中的角点特征与点云数据中的边缘及平面特征数量，量化其可靠性以增强GNN中特征图表示的权重。运行时，模型利用多传感器输入预测局部规划器生成轨迹的成功概率，从而避免潜在碰撞与故障。当复杂室外环境中一个或多个传感器模态不可靠或缺失时，该算法仍能保持稳健的预测性能。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。与现有先进导航方法相比，导航成功率提升10-30%，误报估计降低13-15%。