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.
Figures & tables
Figure 1 : Comparison of localization accuracy across recent LiDAR-based SCR methods . Our method achieves the lowest translation and rotation errors on the QEOxford and NCLT datasets while maintaining real-time inference capability of over 90Hz.
Figure 2 : Comparison of global embeddings. We evaluate the discriminative ability of global embeddings within the same cluster (colored black on left) by measuring their cosine similarity to a scene cluster center ( ⋆ ) on the 15-13-06-37 in the QEOxford dataset. Compared to classification logits-based embeddings (LightLoc [ 15 ] ) that lack fine-grained discrimination by maintaining erroneously high similarity for spatially distant samples, our proposed global embedding provides more discriminative information to the SCR model, yielding high cosine similarity for nearby scans while naturally decreasing as the spatial distance increases.
Figure 3 : Analysis of LightLoc [ 15 ] . We analyze the relationship between the regression error & inlier ratio and semantic meaning in the 15-13-06-37 sequence of the QEOxford. To categorize the query points according to semantic meaning, we employ a pretrained SphereFormer [ 32 ] . Features from the dynamic categories (e.g. vehicles, pedestrians) suffer from higher errors and lower inlier ratios, whereas features from static regions show the opposite trend.
Figure 4 : Overview of our ReLoc framework. We adopt a two-stage training strategy. In Stage 1, the global embedding module is trained using 6-DOF pose supervision and contrastive regularization to learn context-aware scene representations. The learned weights are then transferred to Stage 2, where the global embeddings are concatenated with enhanced features through the Local Feature Enhancement Module, and an MLP-based regressor is trained to predict scene coordinates.
Figure 5 : Architecture of the global embedding module. Learnable context tokens interact with voxel features via cross-attention to capture scene-aware context and are then aggregated by an MLP-Mixer to produce a compact global embedding for downstream regression.
Figure 6 : Attention behavior of the feature enhancement module. Red lines indicate the top-100 referenced features, indicating that during the self-attention, the query feature ( ⋆ ) primarily refers to features from geometrically consistent structures (e.g. walls).
Method
15-13-06-37
17-13-26-39
17-14-03-00
18-14-14-42
Avg. ( ↓ )
SGLoc [ 13 ]
1.79/1.67
1.81/1.76
1.33/1.59
1.19/1.39
1.53/1.60
LiSA [ 14 ]
0.94/1.10
1.17/1.21
0.84/1.15
0.85/1.11
0.95/1.14
LightLoc [ 15 ]
0.82/1.12
0.85/1.07
0.81/1.11
0.82 /1.16
0.83/1.12
GTRLoc [ 16 ]
0.77/1.02
0.77/1.01
0.67/1.01
0.80 / 1.07
0.75/1.03
ReLoc (ours)
0.73/0.87
0.64/0.83
0.57/0.85
0.80/0.94
0.69/0.87
Table I : Quantitative localization results on the QEOxford dataset. Mean position error (m) and mean orientation error ( ∘ ) are reported. Best and second results are in bold and underlined.
Figure 7 : Qualitative localization result on 17-13-26-39 of the QEOxford dataset. Position errors are visualized using a heatmap capped at 5m, where our method exhibits consistently lower errors (dots colored in blue ) along the trajectory compared to prior approaches.
Method
2012-02-12
2012-02-19
2012-03-31
2012-05-26
Avg. ( ↓ )
SGLoc [ 13 ]
1.20/3.08
1.20/3.05
1.12/3.28
3.48/4.43
1.75/3.46
LiSA [ 14 ]
0.97/ 2.23
0.91/ 2.09
0.87/ 2.21
3.11/ 2.72
1.47/ 2.31
LightLoc [ 15 ]
0.98/2.76
0.89/2.51
0.86/2.67
3.10/3.26
1.46/2.80
GTRLoc [ 16 ]
0.95 /2.53
0.82 /2.45
0.82 /2.52
3.01 /2.99
1.40 /2.62
ReLoc (ours)
0.88 / 2.25
0.76/2.01
0.65/2.18
2.46/2.66
1.19/2.28
Table II : Quantitative localization results on the NCLT dataset . Mean position error (m) and mean orientation error ( ∘ ) are reported. Best and second results are in bold and underlined.
Figure 8 : Qualitative localization results on 2012-02-12 of the NCLT dataset. Position errors are visualized as done in Fig. 7 . Our method shows the reduced position error (dots colored in blue ) along the trajectory compared to prior approaches.
Global Embedding
Local Feature Enhancement
QEOxford ( ↓ )
NCLT ( ↓ )
mean error(m/°)
mean error(m/°)
1.32/1.69
2.72/4.18
✓
0.74/0.94
1.25/2.50
✓
✓
0.69/0.87
1.19/2.28
Table III : Ablation study on the QEOxford and NCLT datasets.
Median regression error ( Lscr ) / Inlier ratio (m / %) ( ↓ / ↑ )
Class
w/o fE
w/ fE
Class
w/o fE
w/ fE
manmade
2.73/55.8
2.24/66.9
terrain
2.40/59.6
2.03/68.8
vegetation
2.09/69.8
1.81/78.4
sidewalk
2.44/61.5
2.04/71.9
car
3.53/41.4
3.07/48.1
truck
4.71/29.2
4.53/31.9
Table IV : Impact of the local feature enhancement module ( fE ) on the 17-13-26-39 sequence of the QEOxford dataset.
K
128 (default)
256
512
β
0.1 (default)
0.5
1.0
m/ ∘
0.69/0.87
0.70/0.89
0.75/0.88
m/ ∘
0.69/0.87
0.82/0.87
0.92/0.89
Table V : Ablation studies of hyperparameters ( K and β ) on the QEOxford dataset. We report mean rotation and translation error of all the test sequences.
LiDAR-based Scene Coordinate Regression (SCR) maps point clouds directly to 3D scene coordinates, enabling precise 6-DoF localisation without explicit map retrieval. However, existing methods produce deterministic predictions, discarding aleatoric uncertainty that could improve robustness and downstream decision-making. We present UQ-Loc, which extends the LightLoc architecture with an anisotropic Gaussian covariance head that predicts a full 3x3 positive-definite covariance matrix per voxel. Training uses a Negative Log-Likelihood (NLL) loss augmented with a kNN-based spatial smoothness regulariser, while inference employs a modified SC2-PCR solver with uncertainty-weighted seed scoring and a Mahalanobis-distance inlier test. We adopt Expected Calibration Error (ECE) as a principled metric for evaluating the quality of the predicted uncertainty. Experiments demonstrate that UQ-Loc achieves consistent improvement in 6-DoF localization accuracy while producing well-calibrated covariances.
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