Sequential Testing for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments

• URL: http://arxiv.org/abs/2512.09447v1
• Date: Wed, 10 Dec 2025 09:20:09 GMT
• Title: Sequential Testing for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments
• Authors: Jaehyun Kim, Seungwon Choi, Tae-Wan Kim,
• Abstract summary: We propose a multi-frame loop closure verification method that formulates LiDAR loop closure as a truncated Sequential Probability Ratio Test (SPRT)<n>Instead of deciding from a single descriptor comparison or using fixed thresholds with late-stage Iterative Closest Point (ICP) vetting, the verifier accumulates a short temporal stream of descriptor similarities between a query and each candidate.<n>This precision-first policy is designed to suppress false positives in structurally repetitive indoor environments.
• Score: 12.304166871828777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a descriptor-agnostic, multi-frame loop closure verification method that formulates LiDAR loop closure as a truncated Sequential Probability Ratio Test (SPRT). Instead of deciding from a single descriptor comparison or using fixed thresholds with late-stage Iterative Closest Point (ICP) vetting, the verifier accumulates a short temporal stream of descriptor similarities between a query and each candidate. It then issues an accept/reject decision adaptively once sufficient multi-frame evidence has been observed, according to user-specified Type-I/II error design targets. This precision-first policy is designed to suppress false positives in structurally repetitive indoor environments. We evaluate the verifier on a five-sequence library dataset, using a fixed retrieval front-end with several representative LiDAR global descriptors. Performance is assessed via segment-level K-hit precision-recall and absolute trajectory error (ATE) and relative pose error (RPE) after pose graph optimization. Across descriptors, the sequential verifier consistently improves precision and reduces the impact of aliased loops compared with single-frame and heuristic multi-frame baselines. Our implementation and dataset will be released at: https://github.com/wanderingcar/snu_library_dataset.

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