MVP-SLAM: Multi-Camera Visual-Inertial Floorplan-Prior SLAM
Authors: Asier Bikandi-Noya, Miguel Fernandez-Cortizas, Muhammad Shaheer, Holger Voos, Jose Luis Sanchez-Lopez
Organizations: Authors are with the Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg. · Holger Voos is also associated with the Faculty of Science, Technology, and Medicine, University of Luxembourg, Luxembourg.
Indoor building construction sites are demanding environments for visual SLAM, where variable lighting and repetitive, low-textured structures make the system drift over long trajectories, though structural elements such as walls remain distinguishable despite these conditions. These buildings are constructed according to their as-planned floor plans, available from the design phase, and although the actual as-built site can differ from this design, floor plans still provide a metric reference, both to localize the system in the building and to correct drift. Existing methods often use the floor plan to correct an already-built trajectory offline, and those that instead correct it online typically rely on depth sensors. We instead present MVP-SLAM, an online visual-inertial SLAM on two opposite-facing fisheye cameras that corrects drift from cameras alone by matching walls detected in its map to the floor plan, through a drift-aware policy. A multi-stage integration then turns each matched pair incrementally into a persistent correction, so the trajectory stays corrected and localized within the floor plan as it is built. MVP-SLAM was validated on the multi-floor construction sites of the Hilti-Trimble SLAM Challenge 2026, ranking 2nd of 22 teams in the Localization task (0.29 m mean RMSE) and 5th of 62 teams in the SLAM task (0.24 m), the top-ranked one in both tasks among those that operate online, integrate the floor plan, and localize within it.
Figures & tables
Fig. 1 : MVP-SLAM on a Hilti construction sequence, matching detected walls to the floor plan and correcting the estimated trajectory against ground truth.
Fig. 2 : System architecture of MVP-SLAM. Sensor and floor-plan inputs (left) feed a three-module pipeline whose optimized state is fed back into the SLAM system as persistent plan priors, producing the plan-aligned trajectory estimate (right).
Fig. 3 : Incremental multi-stage integration of a matched wall, preserving earlier corrections.
Floors and Sequences
Ground Floors
Upper Floors
Underground Levels
Floor 1
Floor 2
Floor EG
Floor 3
Floor 4
Floor 5
Floor 6
Floor 7
Floor UG1
Floor UG2
07-07
12-02
12-02
12-03
10-16
12-02a
12-02b
05-19
12-02
05-19
12-02
12-02
06-18
07-07
12-02a
12-02b
12-02a
12-02b
12-03
05-19
06-18
12-02a
12-02b
12-03
12-02
Average
Duration ( sec. )
133.7
276.3
143.7
149.9
242.9
126.4
162.5
115.5
132.7
91.9
193.4
160.5
69.3
73.0
170.7
124.6
117.9
155.1
198.3
205.5
165.8
254.8
219.7
134.4
223.1
161.7
Length ( m )
157.8
321.8
154.8
138.8
240.4
114.9
158.6
128.2
148.8
97.8
214.1
174.9
79.4
72.2
210.6
145.8
145.4
197.1
152.6
262.5
222.6
351.7
322.5
119.1
292.1
185.0
Localization
Map-It Ralph [ 14 ]
71.92
39.68
577.06
233.11
41.86
87.98
320.64
103.89
182.71
38.67
49.77
82.38
49.74
41.31
50.59
109.38
301.10
105.34
69.70
95.13
147.77
264.79
88.25
127.67
–
136.69
TABLE I : Absolute Pose Error (APE) RMSE ( cm ) on Hilti–Trimble SLAM Challenge 2026 sequences, as reported on the official challenge leaderboard on the day of the challenge. Bold and underline denote best and second-best results per column per task; dashes (–) mark unavailable entries for Localization.
Parameter
Symbol
Value
Description
Drift-aware association (Sec. III-C )
score weights
wa,d,f
6 rad -1 , 1 m -1 , 1
angle, dist., overlap
gate ramp
d0,αℓ
1 m, 0.1
offset, drift growth
gate cap
cext/int
5 , 2.5 m
ext./int. distance cap
dist. saturation
dsat
1 m
score distance cap
saturation range
ℓmin/max
15 , 40 m
score ramp
TABLE II : Key parameters of MVP-SLAM.
Fig. 4 : Trajectory comparison in the Localization task, in the floor-plan frame, on three representative sequences.
Reduced
Full
Floor
Sequence
tmetric
Cov.
APE
tmetric
Cov.
APE
Ground
Floor 1 (07-07)
3.1
100
1.38
2.5
100
1.31
Floor EG (10-16)
2.5
100
3.02
2.2
100
2.44
Upper
Floor 3 (05-19)
2.8
100
0.58
2.5
100
0.55
Floor 6 (12-02a)
2.5
100
1.01
2.5
100
0.76
Under
Floor UG1 (06-18)
6.6
96.7
28.0
2.5
100
2.07
TABLE III : Initialization and robustness ablation for Ours (w/o plan) : 3 repeats per sequence, both configurations initialized every run. tmetric : median time to metric scale (s); Cov.: mean coverage (%); APE: median Localization error (m).
Detection
Matching
Floor
Sequence
P
R
F1
P
R
F1
Ground
Floor 1 (07-07)
94.4
50.0
65.4
100.0
94.1
97.0
Floor EG (10-16)
100.0
28.6
44.4
100.0
100.0
100.0
Upper
Floor 3 (05-19)
100.0
57.9
73.3
100.0
100.0
100.0
Floor 6 (12-02a)
84.6
35.5
50.0
90.9
100.0
95.2
Under
Floor UG1 (06-18)
100.0
37.8
54.9
100.0
100.0
100.0
TABLE IV : Wall-detection and wall-matching performance (precision P, recall R, F1, in percent). Overall pools counts.
Hilti AG, Corporate Research & Technology, Schaan, Liechtenstein · University of Oxford, Dynamic Robot Systems Group, Oxford, UK · ETH Zürich, Robotics Systems Lab, Zürich, Switzerland +2