论文标题
部分可观测时空混沌系统的无模型预测
Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder
论文作者
论文摘要
感知和定位对于自动递送车辆至关重要，由于其精确的距离测量能力，大多数是从3D激光雷达传感器估计的。本文提出了一种从图像传感器获得实时伪点云的策略，而不是激光传感器。我们提出了一种使用不同深度估计器来获得伪点云（如LiDAR）以获得更好性能的方法。此外，深度估计器的训练和验证策略采用了立体图像数据，以估计更准确的深度估计以及点云结果。我们生成深度映射的方法在Kitti基准测试上的表现优于Kitti基准，同时产生的点云要比其他方法要快得多。
Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to use different depth estimators to obtain pseudo point clouds like LiDAR to obtain better performance. Moreover, the training and validating strategy of the depth estimator has adopted stereo imagery data to estimate more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms on KITTI benchmark while yielding point clouds significantly faster than other approaches.
