PinNet：用于LiDAR SLAM回环检测的具有几何嵌入的关键点感知局部描述符
PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM
AI总结：
提出PinNet网络，通过关键点生成和平面几何自注意力模块增强描述符判别性，实现基于点云的回环检测与配准，在多个数据集上验证了优越性能。
摘要：
回环检测对于减少漂移并在大规模环境中构建全局一致的地图至关重要。然而，仅依靠来自LiDAR传感器的几何信息进行可靠的回环检测仍然具有挑战性，因为难以构建具有判别性的几何特征。我们提出了PinNet，一种从点云生成局部几何描述符的神经网络，用于地点识别和扫描到扫描的配准。PinNet结合了一个生成关键点及其对应描述符的神经网络，以及一个基于平面的几何自注意力模块，该模块建模关键点间的空间关系，以增强描述符的判别性，用于回环检测和点云配准。该方法在多个使用不同LiDAR传感器收集的数据集上进行了全面评估。实验结果表明，在不同环境中具有强大的地点识别性能、精确的相对位姿估计以及成功的单次定位。
英文摘要：
Loop closure is essential to reduce drift and build globally consistent maps in large-scale environments. However, reliable loop closure with only geometric information from, e.g., a LiDAR sensor, remains challenging due to the difficulty of constructing discriminative geometric features. We present PinNet, a neural network that produces local geometric descriptors from point clouds for place recognition and scanto-scan registration. PinNet incorporates a neural network that generates keypoints and their corresponding descriptors, together with a plane-based geometric self-attention module that models inter-keypoint spatial relationships to enhance descriptor discriminability for loop-closure detection and point-cloud registration. The approach is comprehensively evaluated on multiple datasets collected with different LiDAR sensors. Experimental results demonstrate strong place-recognition performance, precise relative pose estimation, and successful single-shot localization in different environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
