Organizations: Fujian Key Laboratory of Urban Intelligent Sensing and Computing, and with Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen, FJ 361005, P.R. China · School of Engineering Mathematics and Technology, University of Bristol, Bristol BS8 1TH, United Kingdom · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical deployment. Recent works improve training efficiency by decoupling SCR into a scene-agnostic backbone and scene-specific prediction heads, where the backbone is pretrained on source datasets and frozen for new scenes, and only lightweight heads are optimized. However, we find that this paradigm heavily depends on the pretrained backbone. Existing decoupled methods can match conventional SCR methods fully optimized for each new scene when LiDAR configurations are similar to those used during backbone pretraining, but their accuracy drops noticeably on datasets collected with different LiDAR sensors. This suggests that efficient LiDAR localization requires representations that capture stable scene geometry across LiDAR configurations. Motivated by this observation, we propose LightLoc++, a sensor-robust and efficient outdoor LiDAR localization framework. To support sensor-robust representation learning, we introduce SULID, a synchronized urban multi-LiDAR dataset with representative 32-, 64-, and 128-beam rotating LiDARs, extensive cross-sensor overlap, and diverse urban scenes. Using SULID, we pretrain a sensor-robust backbone through cross-sensor consistency learning. LightLoc++ further preserves efficient new-scene learning by incorporating sample classification guidance and redundant sample downsampling, which reduce regression ambiguity and computational redundancy in large-scale outdoor scenes. Extensive experiments on multiple outdoor LiDAR localization benchmarks demonstrate that LightLoc++ achieves state-of-the-art localization performance with the lowest new-scene training cost among compared methods. Code and dataset will be made available at https://github.com/liw95/LightLoc-PlusPlus.
Figures & tables
Fig. 1: LiDAR localization performance vs. training efficiency. The figure compares normalized localization accuracy and training efficiency across multiple LiDAR benchmarks collected with different sensors [ 3 , 4 , 5 , 6 , 7 ] . Larger values indicate better performance. LightLoc++ achieves consistently strong localization performance across diverse sensing modalities while maintaining high training efficiency.
Datasets
Reference
Diverse LiDAR
Cross-LiDAR Consistency
Diverse Scene
Scene Types
Total Distance
KITTI [ 63 ]
CVPR’12
✗
✗
✓
Open
44 Km
NCLT [ 64 ]
IJRR’16
✗
✗
✗
Structured
147 Km
Oxford [ 5 ]
ICRA’20
✗
✗
✗
Open
280 Km
MulRan [ 4 ]
ICRA’20
✗
✗
✓
Open, Structured
123 Km
Apollo-SouthBay [ 22 ]
CVPR’19
✗
✗
✓
Open
381 Km
nuScenes [ 23 ]
CVPR’20
✗
✗
✓
Open
242 Km
TABLE I: Comparison of LiDAR localization datasets. LiDAR diversity refers to the use of sensors with different configurations (e.g., beam numbers and sampling densities), while cross-sensor consistency indicates aligned observations of the same scene across sensors. Scene types are categorized as open (sparse urban environments with weak structural constraints), structured (regular layouts with repeated geometric patterns), dense (cluttered environments with high structural density and strong occlusions), and degenerate (low-feature or geometrically ambiguous environments with limited observability).
Fig. 2: Overview of the SULID dataset. (a) Aerial views of the collected scenes, including Campus-A , Campus-B , Urban-Road , and Urban-Hub , with LiDAR trajectories overlaid, covering diverse urban environments. (b) The data collection platform is equipped with multiple LiDAR sensors of different configurations, along with their spatial arrangement and coordinate systems, enabling synchronized multi-LiDAR observations.
Sensor
Resolution (V × H)
FOV (H × V)
Range
FPS
Ouster OS1-128
128 × 2048
360 ∘ × 45 ∘
120m
10Hz
Ouster OS1-64
64 × 1024
360 ∘ × 45 ∘
120m
10Hz
Hesai XT-32
32 × 1800
360 ∘ × 31 ∘
120m
10Hz
TABLE II: Specifications of multi-LiDAR sensors used in SULID.
Fig. 3: Cross-sensor point-cloud observations in SULID. Point clouds captured simultaneously at the same location by Ouster OS1-128, Ouster OS1-64, and Hesai XT-32. The sensors exhibit different sampling densities and spatial patterns while preserving largely overlapping scene structures, providing shared observations for cross-sensor representation learning.
Fig. 4: Representative scene types in SULID. Each column corresponds to a scene type, including structured, open, and dense environments. The top row shows accumulated point cloud maps (submaps), while the bottom row presents single-frame point clouds. Structured environments exhibit repetitive geometric patterns, open environments contain sparse structures with weak geometric constraints, and dense environments are characterized by strong occlusions and clutter. The red line indicates the driving trajectory. The red dot highlights the reference position.
Fig. 5: Range-dependent point distributions across different scene types. Point counts are computed within distance bins using Ouster OS1-128 scans and normalized by the nearest-range bin. The structured, open, and dense scenes exhibit distinct distribution patterns, reflecting differences in geometric sparsity, structural density, and occlusion characteristics.
Fig. 6: Training pipeline of LightLoc++. (a) In sensor-robust backbone learning, synchronized scans are transformed into a unified LiDAR coordinate system and fed into a shared backbone with multi-scene regression heads. Local and global consistency losses are imposed across synchronized LiDAR observations to learn sensor-robust representations. (b) In new-scene learning, the pretrained backbone is frozen, while lightweight scene-specific heads are trained with SCG and RSD for efficient SCR. LPC and WPC denote local point coordinates in the unified LiDAR coordinate system and world point coordinates, respectively. SC denotes sample classification.
Fig. 7: Network architectures used in LightLoc++. LightLoc++ uses the same backbone and prediction-head architectures as LightLoc [ 19 ] . Cls. head and Reg. head denote the sample classification head and the scene coordinate regression head, respectively. The backbone extracts scene-agnostic point features, the classification head provides spatial guidance for SCG, and the regression head predicts scene coordinates for pose estimation. Here, k denotes the number of spatial clusters used for sample classification in SCG.
Methods
Reference
Training Time
15-13-06-37
17-13-26-39
17-14-03-00
18-14-14-42
Average
PointLoc [ 54 ]
Sens. J.’22
125 hrs.
10.75 / 2.36
11.07 / 2.21
11.53 / 1.92
9.82 / 2.07
10.79 / 2.14
PosePN [ 20 ]
PR’22
5 hrs.
9.47 / 2.80
12.98 / 2.35
8.64 / 2.19
6.26 / 1.64
9.34 / 2.25
PosePN++ [ 20 ]
PR’22
11 hrs.
4.54 / 1.83
6.44 / 1.78
4.89 / 1.55
4.64 / 1.61
5.13 / 1.69
STCLoc [ 56 ]
TITS’23
20 hrs.
5.14 / 1.27
6.12 / 1.21
5.32 / 1.08
4.76 / 1.19
5.34 / 1.19
NIDALoc [ 57 ]
TITS’24
20 hrs.
3.71 / 1.50
5.40 / 1.40
3.94 / 1.30
4.08 / 1.30
4.28 / 1.38
HypLiLoc [ 55 ]
CVPR’23
17 hrs.
5.03 / 1.46
4.31 / 1.43
3.61 / 1.11
2.61 / 1.09
3.89 / 1.27
TABLE III: Quantitative results on the QEOxford dataset. Mean ATE (m) / ARE ( ∘ ) are reported. . Best results in bold , second best results underlined . Training time is also included.
Methods
Reference
Training Time
2020-12-01
2021-02-02
2021-03-02
2021-04-29
Average
PointLoc [ 54 ]
Sens. J.’22
26 hrs.
14.45 / 1.55
12.73 / 1.59
12.00 / 1.40
13.00 / 1.48
13.05 / 1.57
PosePN [ 20 ]
PR’22
6 hrs.
15.59 / 4.18
5.58 / 0.99
4.59 / 0.86
5.82 / 0.88
7.90 / 1.73
PosePN++ [ 20 ]
PR’22
8 hrs.
7.69 / 1.45
4.87 / 0.98
4.53 / 0.89
5.14 / 0.87
5.56 / 1.01
STCLoc [ 56 ]
TITS’23
13 hrs.
4.64 / 0.73
4.56 / 0.80
4.38 / 0.80
4.71 / 0.63
4.57 / 0.74
NIDALoc [ 57 ]
TITS’24
21 hrs.
5.01 / 0.87
4.87 / 0.82
4.87 / 0.82
4.78 / 0.71
4.88 / 0.81
HypLiLoc [ 55 ]
CVPR’23
8 hrs.
4.46 / 0.70
1.86 / 0.71
1.66 / 0.53
1.57 / 0.44
2.39 / 0.60
TABLE V: Quantitative results on the Boreas dataset. Mean ATE (m) / ARE ( ∘ ) are reported. . Best results in bold , second best results underlined . Training time is also included.
Methods
Reference
Training Time
DCC
PointLoc [ 54 ]
Sens. J.’22
725 mins.
16.38 / 5.16
PosePN [ 20 ]
PR’22
70 mins.
18.30 / 7.68
PosePN++ [ 20 ]
PR’22
200 mins.
6.64 / 3.43
STCLoc [ 56 ]
TITS’23
230 mins.
7.89 / 3.25
NIDALoc [ 57 ]
TITS’24
321 mins.
5.87 / 3.39
HypLiLoc [ 55 ]
CVPR’23
110 mins.
8.96 / 2.64
TABLE VI: Quantitative results on the MulRan dataset. Mean ATE (m) / ARE ( ∘ ) are reported. . Best results in bold , second best results underlined . Training time is also included.
Methods
Training Time
Library
Sports
PointLoc [ 54 ]
288 mins.
6.25 / 3.15
4.45 / 4.62
PosePN [ 20 ]
28 mins.
7.54 / 4.65
4.23 / 5.26
PosePN++ [ 20 ]
70 mins.
4.70 / 2.75
5.82 / 4.93
STCLoc [ 56 ]
98 mins.
8.40 / 5.09
5.92 / 4.87
NIDALoc [ 57 ]
113 mins.
3.15 / 2.08
2.45 / 3.66
HypLiLoc [ 55 ]
39 mins.
5.81 / 2.07
4.87 / 3.65
TABLE VII: Quantitative results on the HeRCULES dataset. Mean ATE (m) / ARE ( ∘ ) are reported. The best and second-best results are highlighted in bold and underline , respectively. Average training time across two scenes is also included.
Fig. 8: Visualization results of predicted trajectories on QEOxford, Boreas, MulRan, and HeRCULES. The ground truth and prediction are black and red lines, respectively. The star denotes the first frame. The caption of each subfigure shows the mean ATE (m) and ARE ( ∘ ). For each trajectory, we highlight the best and second-best results.
Pretraining
QEOxford
Boreas
MulRan
HeRCULES
KITTI
1.50 / 1.73
2.75 / 1.17
5.33 / 4.32
2.09 / 2.98
nuScenes
0.83 / 1.12
2.12 / 1.24
4.76 / 2.77
2.27 / 2.92
HeliPR
0.95 / 1.27
1.31 / 0.82
3.62 / 2.50
2.13 / 2.95
SULID
0.72 / 1.03
0.98 / 0.66
3.50 / 2.39
2.02 / 2.81
+ Lglobal
0.69 / 1.02
0.92 / 0.66
3.56 / 2.41
1.83 / 2.62
+ Llocal
0.70 / 0.99
0.87 / 0.64
3.36 / 2.18
1.75 / 2.60
TABLE VIII: Effect of multi-LiDAR representation learning. We evaluate localization performance across multiple LiDAR benchmarks using encoders pretrained under different settings. Results are reported as mean ATE (m) / ARE ( ∘ ).
Method
O → O
O → A
O → V
Average
Reference Localization Methods
LiSA
0.88 / 0.70
2.18 / 2.62
101.94 / 47.56
41.39 / 14.35
RALoc
1.95 / 1.01
22.05 / 26.01
246.13 / 120.23
90.04 / 49.08
FlashMix
6.76 / 5.69
245.61 / 73.77
164.89 / 100.68
104.32 / 60.05
Our Framework with Different Pretrained Backbones
KITTI
1.74 / 1.14
38.22 / 25.63
213.73 / 103.25
84.56 / 43.34
TABLE IX: Cross-Sensor Generalization. LiSA, RALoc, and FlashMix are included as reference localization methods to indicate the cross-sensor generalization capability of existing approaches. For our framework, SCG and RSD are disabled, and only the pretrained encoder is varied. Results are reported as mean ATE (m) / ARE ( ∘ ).
Fig. 9: Cross-sensor localization generalization on the HeLiPR DCC sequence. Despite the substantial differences in sensing characteristics and fields of view, our method maintains accurate localization across Ouster, Aeva, and Velodyne sensors without fine-tuning. The star denotes the first frame.
Backbone
SCG
RSD
Training Time
Mean Error [m/ ∘ ]
1
5.7 hrs.
1.02 / 1.23
2
✓
2.4 hrs.
0.84 / 1.12
3
✓
✓
1.3 hrs.
0.71 / 0.95
4
✓
✓
2 hrs.
0.86 / 1.15
5
✓
✓
✓
1 hrs.
0.70 / 0.99
TABLE X: Ablation study . Training time required to achieve comparable accuracy on QEOxford. Backbone : multi-LiDAR backbone. SCG : sample classification guidance for regression learning. RSD : redundant sample downsampling for filtering well-learned samples during training.
Clusters
5
15
25
50
100
Acc.
100
99.89
99.76
99.62
99.17
Err.
0.76/1.06
0.73/1.05
0.70/0.99
0.73/1.02
0.77/1.07
TABLE XI: Effect of cluster number k in SCG. We report the classification accuracy (%) and localization error (ATE/ARE) on the QEOxford dataset.
Sampling
Random
Uniform
RSD
15 %
0.84 / 1.21
0.77 / 0.98
0.71 / 0.97
25 %
0.96 / 1.27
0.83 / 1.00
0.70 / 0.99
35 %
1.12 / 1.43
1.04 / 1.32
0.82 / 1.02
TABLE XII: Impact of different sampling strategies and ratios . We report the mean error [m/ ∘ ] on the QEOxford dataset.
Sequence
Length
Tag
Training
Testing
11-14-02-26
9.37km
Sunny
✓
14-12-05-52
9.22km
Overcast
✓
14-14-48-55
9.04km
Overcast
✓
18-15-20-12
9.04km
Overcast
✓
15-13-06-37
8.85km
Overcast
✓
17-13-26-39
9.02km
Sunny
✓
TABLE XIII: Dataset Descriptions on the Oxford and QEOxford dataset.
Fig. 10: Visualization of the K-Means-based spatial partitions in SULID. Different colors denote different scene partitions, and the number in parentheses indicates the number of synchronized groups.
Scene Coordinate Regression (SCR) has recently emerged as a promising approach for LiDAR-based localization, achieving accurate localization without requiring an explicit 3D map. Despite their effectiveness, existing SCR methods rely on scene classification-based global embedding that struggles to provide fine-grained discrimination among nearby locations. Moreover, their reliance on uniform sampling of local features during training assigns equal importance to all points, thereby inadvertently propagating features from dynamic objects or unstable regions and potentially degrading training stability. In this paper, we present ReLoc, a revamped SCR architecture that can effectively address these limitations. First, we redesign the global embedding module by combining learnable context tokens with a feature aggregator to capture richer and more discriminative scene context. Second, we introduce an attention-based local feature enhancement module to mitigate the impact of noisy local features while encouraging context-consistent structures, yielding more robust local feature representations. Experimental results on two large-scale outdoor datasets demonstrate that our approach achieves state-of-the-art accuracy over previous SCR-based methods while maintaining real-time inference performance.
Heejoon Moon, Yurim Cho, Je Hyeong Hong
Department of Artificial Intelligence, Hanyang University · Department of Electronic Engineering, Hanyang University
Global LiDAR localization is a fundamental task for autonomous navigation systems. Recent methods perform Scene Coordinate Regression (SCR) and achieve superior accuracy over Absolute Pose Regression (APR) solutions by predicting dense 3D world coordinates. However, SCR approaches introduce two major bottlenecks: severe computational inefficiency from processing raw 3D geometries and significant performance degradation under varying sensor viewpoints. To address these limitations, we present MB-Loc, a lightweight and viewpoint-robust SCR framework. Instead of relying on heavy 3D convolutions, we project the input LiDAR scan into a 2.5D Multi-planar Bird's-Eye View (BEV) representation. By slicing the point-cloud along the Z-axis and mapping signed depths into discrete 2D planes, MB-Loc retains essential 3D geometric structures while exploiting the computational tractability of standard 2D CNNs. To handle the inherent sparsity of outdoor LiDAR, we introduce a KL-regularized latent bottleneck that explicitly models spatial uncertainty without injecting stochastic noise. Finally, to ensure rotation robustness, we apply 3D spatial augmentations prior to planar projection, forcing the network to implicitly learn viewpoint-invariant features. We perform extensive experiments on the publicly available NCLT dataset and demonstrate that our proposed method outperforms the current state-of-the-art. Operating at real-time inference speeds, MB-Loc significantly outperforms traditional 3D-SCR architectures in computational efficiency.
LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches often encounter limitations in dynamic or ambiguous scenarios, as they typically prioritize single-frame inference, leaving the potential of spatio-temporal consistency across scans not fully explored. In this paper, we propose a Temporal-aware Localization framework (TempLoc) designed to enhance the robustness of outdoor localization by effectively modeling sequential consistency. Specifically, a Global Coordinate Estimation module is first introduced to predict point-wise global coordinates and associated uncertainties for each LiDAR scan. A Prior Coordinate Generation module is then presented to estimate inter-frame point correspondences by the attention mechanism. Lastly, an Uncertainty-Guided Coordinate Fusion module is deployed to integrate both predictions of point correspondence in an end-to-end fashion, yielding a more temporally consistent and accurate global 6-DoF pose. Experimental results on the NCLT and Oxford RobotCar benchmarks show that our TempLoc outperforms state-of-the-art methods by a large margin, demonstrating the effectiveness of temporal-aware correspondence modeling in LiDAR relocalization.
Minghang Zhu, Zhijing Wang, Yuxin Guo +5
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University, Xiamen, China · School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom