PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM

• URL: http://arxiv.org/abs/2606.28637v1
• Date: Fri, 26 Jun 2026 22:48:47 GMT
• Title: PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM
• Abstract summary: PinNet is a neural network that produces local geometric descriptors from point clouds for place recognition and scanto-scan registration.<n>Results show strong place-recognition performance, precise relative pose estimation, and successful single-shot localization.
• Score: 1.826848871278733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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