针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编...针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编码,采用最大池化与平均池化叠加的方法将点云的显著特征与细节特征映射为柱体特征;其次,针对算法对伪图特征关注与利用不充分的问题,引入坐标注意力(coordinate attention,CA)机制和残差连接的伪图特征提取模块(attention and residual second block,ARSB),将深层与浅层特征图进行融合,优化算法梯度,增强算法对有效目标的关注度.试验结果表明:改进算法对全局点云检测精度较高,平均精度优于PointPillars、稀疏到稠密3D目标检测器(STD)等点云目标检测算法,在汽车类别上的检测精度优势明显,检测速度较快,符合实时性要求.展开更多
In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to th...In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to the common applications of manually designed keypoint descriptors for coarse point cloud registration. The CPN directly consumes a point cloud, divides it into equally spaced 3D voxels and transforms the points within each voxel into a unified feature representation through voxel feature encoding (VFE) layer. Then all volumetric representations are aggregated by Weighted Extraction Layer which selectively extracts features and synthesize into global descriptors and coordinates of control points. Utilizing global descriptors instead of local features allows the available geometrical data to be better exploited to improve the robustness and precision. Specifically, CPN unifies feature extraction and clustering into a single network, omitting time-consuming feature matching procedure. The algorithm is tested on point cloud datasets generated from CT images. Experiments and comparisons with the state-of-the-art descriptors demonstrate that CPN is highly discriminative, efficient, and robust to noise and density changes.展开更多
Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly,...Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly, to preserve the prior distributions and local geometric structures, we fuse learned hidden features with handcrafted features in the beginning. Secondly, we further extract and aggregate adaptively weighted features concerning density and relative spatial information from these fused features, named Weighted Aggregation with Density Estimation (WADE) module. Then, we conduct the WADE block iteratively to group the latent manifold structures. Finally, comparison results on two public datasets Oxford Robotcar and KITTI show that the proposed approach exceeds the comparison approaches on recall rate averagely 7% - 8%.展开更多
文摘针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编码,采用最大池化与平均池化叠加的方法将点云的显著特征与细节特征映射为柱体特征;其次,针对算法对伪图特征关注与利用不充分的问题,引入坐标注意力(coordinate attention,CA)机制和残差连接的伪图特征提取模块(attention and residual second block,ARSB),将深层与浅层特征图进行融合,优化算法梯度,增强算法对有效目标的关注度.试验结果表明:改进算法对全局点云检测精度较高,平均精度优于PointPillars、稀疏到稠密3D目标检测器(STD)等点云目标检测算法,在汽车类别上的检测精度优势明显,检测速度较快,符合实时性要求.
文摘In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to the common applications of manually designed keypoint descriptors for coarse point cloud registration. The CPN directly consumes a point cloud, divides it into equally spaced 3D voxels and transforms the points within each voxel into a unified feature representation through voxel feature encoding (VFE) layer. Then all volumetric representations are aggregated by Weighted Extraction Layer which selectively extracts features and synthesize into global descriptors and coordinates of control points. Utilizing global descriptors instead of local features allows the available geometrical data to be better exploited to improve the robustness and precision. Specifically, CPN unifies feature extraction and clustering into a single network, omitting time-consuming feature matching procedure. The algorithm is tested on point cloud datasets generated from CT images. Experiments and comparisons with the state-of-the-art descriptors demonstrate that CPN is highly discriminative, efficient, and robust to noise and density changes.
文摘Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly, to preserve the prior distributions and local geometric structures, we fuse learned hidden features with handcrafted features in the beginning. Secondly, we further extract and aggregate adaptively weighted features concerning density and relative spatial information from these fused features, named Weighted Aggregation with Density Estimation (WADE) module. Then, we conduct the WADE block iteratively to group the latent manifold structures. Finally, comparison results on two public datasets Oxford Robotcar and KITTI show that the proposed approach exceeds the comparison approaches on recall rate averagely 7% - 8%.
