All-weather autonomy is critical for autonomous driving, which necessitates reliable localization across diverse scenarios. While LiDAR place recognition is widely deployed for this task, its performance degrades in adverse weather. Conversely, radar-based methods, though weather-resilient, are hindered by the general unavailability of radar maps. To bridge this gap, radar-to-LiDAR place recognition, which localizes radar scans within existing LiDAR maps, has garnered increasing interest. However, extracting discriminative and generalizable features shared between modalities remains challenging, compounded by the scarcity of large-scale paired training data and the signal heterogeneity across radar types. In this work, we propose RLPR, a robust radar-to-LiDAR place recognition framework compatible with single-chip, scanning, and 4D radars. We first design a dual-stream network to extract structural features that abstract away from sensor-specific signal properties (e.g., Doppler or RCS). Subsequently, motivated by our task-specific asymmetry observation between radar and LiDAR, we introduce a two-stage asymmetric cross-modal alignment (TACMA) strategy, which leverages the pre-trained radar branch as a discriminative anchor to guide the alignment process. Experiments on four datasets demonstrate that RLPR achieves state-of-the-art recognition accuracy with strong zero-shot generalization capabilities.
Figures & tables
Fig. 1: Conceptual overview of the proposed RLPR framework. (a) RLPR facilitates robust place recognition across radar types. (b) Existing methods perform symmetric feature alignment. (c) Motivated by the task‑specific asymmetry between radar and LiDAR, we design a two-stage asymmetric cross-modal alignment (TACMA) strategy that enables effective feature alignment.
Fig. 2: The system overview of RLPR. The proposed radar-to-LiDAR place recognition framework consists of a dual-stream network and the two-stage asymmetric cross-modal alignment (TACMA) strategy. The dual-stream network is designed to extract global and local geometric features, while the TACMA strategy exploits cross-modal commonalities for accurate and generalizable retrieval.
Fig. 3: Architecture of the proposed Polar Context Enhancer. It captures global dependencies through two-way scanning along range and azimuth axes, generating an importance map to gate the polar BEV. The corresponding images share the same color scale.
Fig. 4: Representational evolution under pre-training. Modality-specific place recognition pre-training induces relatively larger radar entropy gain with concurrent gains in separability. The polar BEV feature visualizations are also provided.
Methods
MulRan-Riverside
MulRan-DCC
Bor-Clear
AR@1
AR@5
AR@10
max F1
AR@1
AR@5
AR@10
max F1
AR@1
AR@5
AR@10
max F1
KidnappedRadar-CM [ 26 ]
5.452
12.94
20.30
0.1286
9.575
23.94
42.02
0.2222
0.6890
2.493
4.133
0.02755
BEVPlace++-CM [ 18 ]
2.668
10.21
16.94
0.05198
2.660
10.64
21.81
0.07207
0.6562
2.067
3.642
0.01377
LIP-Loc [ 23 ]
7.135
17.40
23.43
0.1024
9.574
26.06
39.36
0.4000
0.7532
2.636
4.759
0.005448
I2P-CMPR [ 30 ]
4.292
19.84
31.73
0.01497
5.851
21.81
31.38
0.08163
3.771
12.12
19.58
0.01304
Radar-to-LiDAR [ 31 ]
31.13
44.54
50.96
0.4748
12.77
24.47
32.98
0.2264
24.51
49.61
61.25
0.3938
TABLE I: Scanning radar-to-LiDAR performance on MulRan & Boreas
Methods
BS
SON
SQ
AR@1
AR@5
AR@10
max F1
AR@1
AR@5
AR@10
max F1
AR@1
AR@5
AR@10
max F1
AutoPlace-CM [ 4 ]
64.65
76.48
79.51
0.7890
0.9072
1.693
2.510
0.01822
0.4706
2.353
3.177
0.03540
BEVPlace++-CM [ 18 ]
1.380
5.087
8.333
0.07576
0.7557
3.023
5.441
0.04545
4.935
16.80
25.73
0.09524
LIP-Loc [ 23 ]
14.64
30.07
37.94
0.2615
2.328
8.620
14.18
0.07122
5.882
18.45
26.79
0.1681
I2P-CMPR [ 30 ]
12.31
26.17
34.45
0.2635
15.84
34.07
42.65
0.3192
18.45
38.43
48.06
0.3518
RLPR (ours)
70.85
80.30
82.79
0.8907
59.31
77.42
83.07
0.7582
47.71
60.63
66.04
0.6893
TABLE II: Phased-array 3D radar-to-LiDAR performance on nuScenes
TABLE IV: Ablation study on place recognition pre-training
Loss
Alignment Strategy
BS
SON
SQ
Riverside
DCC
Bor-Clear
81r
AR@1
AR@5
AR@1
AR@5
AR@1
AR@5
AR@1
AR@5
AR@1
AR@5
AR@1
AR@5
AR@1
AR@5
Triplet
Frozen L
44.29
58.09
32.22
49.58
7.451
23.40
46.40
64.10
45.74
69.68
62.60
68.41
17.95
19.49
Triplet
Both Trainable
61.09
71.37
44.53
61.22
45.71
58.05
56.38
72.85
56.38
78.72
84.84
88.16
26.05
27.94
Triplet
Frozen R
55.89
66.90
37.88
58.52
44.18
56.64
59.05
75.00
55.32
75.00
84.71
88.19
23.45
25.05
MSE
Frozen L
60.08
71.18
27.81
42.50
39.84
56.05
56.61
71.29
39.89
66.49
46.10
52.76
30.67
32.60
MSE
Both Trainable
1.735
6.478
1.183
4.414
2.473
7.173
11.48
23.67
10.11
24.47
0.7218
2.067
1.349
1.606
TABLE V: Ablation study on alignment strategy
Alignment Strategy
Phased-Array 3D Radar (On Average)
Scanning Radar (On Average)
Phased-Array 4D Radar (On Average)
H(L)
H(R)
LPR (AR@1)
RPR (AR@1)
H(L)
H(R)
LPR (AR@1)
RPR (AR@1)
H(L)
H(R)
LPR (AR@1)
RPR (AR@1)
Pre-Alignment
0.9378
1.088
88.26
81.51
1.060
1.184
86.78
88.14
0.9213
0.9670
87.34
78.45
Frozen L
0.9378
1.114
88.26 (-0.00)
81.50 ( -0.01 )
1.060
1.182
86.78 (-0.00)
85.81 ( -2.33 )
0.9213
1.055
87.34 (-0.00)
73.10 ( -5.35 )
Both Trainable
1.215
1.112
81.82 ( -6.44 )
74.00 ( -7.51 )
1.110
1.103
79.58 ( -7.20 )
77.39 ( -10.75 )
1.097
0.9365
82.72 ( -4.62 )
76.91 ( -1.54 )
Frozen R
1.266
1.088
85.41 ( -2.85 )
81.51 (-0.00)
1.156
1.184
85.23 ( -1.55 )
88.14 (-0.00)
1.127
0.9670
83.70 ( -3.64 )
78.45 (-0.00)
TABLE VI: The impact of alignment strategies on modality-specific discriminability
CMP
GAP
CAP
LD
GD
PCE
PCG
BS
SON
AR@1
AR@5
AR@1
AR@5
✗
✗
✓
✓
✗
✗
✗
43.21
61.01
8.827
19.56
✗
✗
✗
✗
✓
✗
✗
51.00
69.07
9.341
23.49
✗
✗
✓
✓
✓
✗
✗
64.26
75.70
55.17
71.70
✓
✗
✗
✓
✓
✓
✗
68.21
78.33
49.03
67.71
✗
✓
✗
✓
✓
✓
✗
58.28
72.46
16.44
30.50
TABLE VII: Ablation study on network architecture
Method
Bor-Snow
AR@1
AR@5
AR@10
AR@20
max F1
MinkLoc3Dv2 [ 16 ]
18.83
31.63
37.45
43.49
0.4606
BEVPlace++ [ 18 ]
67.31
73.62
76.01
79.49
0.8524
Radar-to-LiDAR [ 31 ]
5.158
24.49
48.46
62.68
0.09880
RaLF [ 21 ]
74.79
86.81
90.65
93.88
0.8588
RLPR (ours)
85.52
93.92
95.86
97.58
0.9226
TABLE VIII: Comparison of performance in snowy scenario
Fig. 5: A comparative visualization of radar-to-radar and LiDAR-to-LiDAR place recognition accuracy.
Fig. 6: Visualization of retrieval results in adverse weather. LPR methods are severely degraded by snow-induced noise, leading to consistent retrieval failures, whereas our proposed R2L method remains robust.
Autonomous driving develops vehicles that perceive road scenes, predict interactions, and plan and control safe movement with limited or no human input.
Place recognition is crucial for loop closure detection and global localization in robotics. Although mainstream algorithms typically rely on cameras and LiDAR, these sensors are susceptible to adverse weather conditions. Fortunately, the recently developed 4D millimeter-wave radar (4D radar) offers a promising solution for all-weather place recognition. However, the inherent noise and sparsity in 4D radar data significantly limit its performance. Thus, in this paper, we propose a novel framework called 4DRaL that leverages knowledge distillation (KD) to enhance the place recognition performance of 4D radar. Its core is to adopt a high-performance LiDAR-to-LiDAR (L2L) place recognition model as a teacher to guide the training of a 4D radar-to-4D radar (R2R) place recognition model. 4DRaL comprises three key KD modules: a local image enhancement module to handle the sparsity of raw 4D radar points, a feature distribution distillation module that ensures the student model generates more discriminative features, and a response distillation module to maintain consistency in feature space between the teacher and student models. More importantly, 4DRaL can also be trained for 4D radar-to-LiDAR (R2L) place recognition through different module configurations. Experimental results prove that 4DRaL achieves state-of-the-art performance in both R2R and R2L tasks regardless of normal or adverse weather.
Ningyuan Huang, Zhiheng Li, Zheng Fang
The authors are all with the Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China.
This paper introduces Dr-LiSA, a first-of-its-kind direct method for localizing 2D spinning radar intensity measurements in SE(3) against 3D lidar maps. Radar-lidar localization combines the complementary strengths of the two sensing modalities: radar is robust to adverse weather and precipitation, while lidar provides high-fidelity 3D maps in favourable conditions. However, existing radar-lidar localization methods are restricted to planar SE(2) localization and have generally fallen short of the accuracy achieved by lidar-lidar and even radar-radar systems. A key challenge is the substantial sensing-modality gap between radar and lidar, which observe and represent scene structure in fundamentally different ways. Dr-LiSA bridges this gap using a learned forward model that predicts radar measurements from a lidar submap at a candidate pose, enabling direct photometric alignment of predicted and observed radar scans in SE(3). Dr-LiSA outperforms prior radar-lidar approaches in SE(2) while achieving planar accuracy competitive with state-of-the-art radar-radar localization across more than 90 km of on-road data.
Alex Zhang, Daniil Lisus, Cedric Le Gentil +1
Robotics Institute, University of Toronto, Canada · Mobile Robotics Lab, ETH Zürich, Switzerland
Autonomous vehicles face major perception and navigation challenges in adverse weather such as rain, fog, and snow, which degrade the performance of LiDAR, RADAR, and RGB camera sensors. While each sensor type offers unique strengths, such as RADAR robustness in poor visibility and LiDAR precision in clear conditions, they also suffer distinct limitations when exposed to environmental obstructions. This study proposes LRC-WeatherNet, a novel multi-sensor fusion framework that integrates LiDAR, RADAR, and camera data for real-time classification of weather conditions. By employing both early fusion using a unified Bird's Eye View representation and mid-level gated fusion of modality-specific feature maps, our approach adapts to the varying reliability of each sensor under changing weather. Evaluated on the extensive MSU-4S dataset covering nine weather types, LRC-WeatherNet achieves superior classification performance and computational efficiency, significantly outperforming unimodal baselines in adverse conditions. This work is the first to combine all three modalities for robust, real-time weather classification in autonomous driving. We release our trained models and source code in https://github.com/nouralhudaalbashir/LRC-WeatherNet.
Nour Alhuda Albashir, Lars Pernickel, Danial Hamoud +2
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research, Halmstad, Sweden