# InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization

arXiv: https://arxiv.org/abs/2607.16862 · HTML: https://mlsanity.com/paper/2607.16862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments](https://mlsanity.com/paper/2109.13494/) (2021)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map](https://mlsanity.com/paper/2210.05984/) (2022)
- [HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatiotemporal Variations](https://mlsanity.com/paper/2309.14590/) (2023)
- [KISS-Matcher: Fast and Robust Point Cloud Registration Revisited](https://mlsanity.com/paper/2409.15615/) (2024)
- [Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM](https://mlsanity.com/paper/2309.08086/) (2023)
- [Narrowing your FOV with SOLiD: Spatially Organized and Lightweight Global Descriptor for FOV-constrained LiDAR Place Recognition](https://mlsanity.com/paper/2408.07330/) (2024)
- [HeLiOS: Heterogeneous LiDAR Place Recognition via Overlap-based Learning and Local Spherical Transformer](https://mlsanity.com/paper/2501.18943/) (2025)
- [FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field](https://mlsanity.com/paper/2404.18006/) (2024)
- [Submodular Optimization for Keyframe Selection & Usage in SLAM](https://mlsanity.com/paper/2410.05576/) (2024)
- [A Minimal Subset Approach for Informed Keyframe Sampling in Large-Scale SLAM](https://mlsanity.com/paper/2501.01791/) (2025)
- [TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching](https://mlsanity.com/paper/2602.01501/) (2026)

## Abstract (arXiv)

LiDAR place recognition supports loop closure, relocalization, and multi-agent map management. As robotic platforms increasingly combine LiDARs with different fields of view, resolutions, and scanning patterns, existing descriptors degrade because they are tightly coupled to sensor-specific characteristics. We present InLiER, a learning-free pipeline based on an intermediate tokenization step. Height-sliced keypoints from structural elements receive mixed-radix token IDs encoding height, radial distance, local shape, and azimuth from local 3D geometry, in a compact sub-2KB representation. The same vocabulary is reorganized across three retrieval stages: height-ceiling histogram intersection for fast rotation-invariant shortlisting, binary bitmask alignment for yaw estimation and reranking, and token-guided geometric verification for 6-DoF pose estimation. InLiER achieves state-of-the-art performance on the HeLiPR dataset and in real-world field experiments, among modern handcrafted methods and outperforms the learning-based baseline on most cross-sensor configurations.