FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency

Matěj Boxan; Gabriel Jeanson; Alexander Krawciw; Effie Daum; Xinyuan Qiao; Sven Lilge; Timothy D. Barfoot; François Pomerleau

cs.ROMar 9, 2026

FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency

Authors: Matěj Boxan, Gabriel Jeanson, Alexander Krawciw, Effie Daum, Xinyuan Qiao, Sven Lilge, Timothy D. Barfoot, François Pomerleau

Organizations: Norlab, Universit´e Laval, Quebec City, Canada · University of Toronto Robotics Institute, Toronto, Canada

Abstract

The Forêt Montmorency (FoMo) dataset is a comprehensive multi-season data collection, recorded over the span of one year in a boreal forest. Featuring a unique combination of on- and off-pavement environments with significant environmental changes, the dataset challenges established odometry and SLAM pipelines. Some highlights of the data include the accumulation of snow exceeding 1 m, significant vegetation growth in front of sensors, and operations at the traction limits of the platform. In total, the FoMo dataset includes over 64 km of six diverse trajectories, repeated during 12 deployments throughout the year. The dataset features data from one rotating and one hybrid solid-state lidar, a Frequency Modulated Continuous Wave (FMCW) radar, full-HD images from a stereo camera and a wide lens monocular camera, as well as data from two IMUs. Ground Truth is calculated by post-processing three GNSS receivers mounted on the Uncrewed Ground Vehicle (UGV) and a static GNSS base station. Additional metadata, such as one measurement per minute from an on-site weather station, camera calibration intrinsics, and vehicle power consumption, is available for all sequences. To highlight the relevance of the dataset, we performed a preliminary evaluation of the robustness of a lidar-inertial, radar-gyro, and a visual-inertial localization and mapping techniques to seasonal changes. We show that seasonal changes have serious effects on the re-localization capabilities of the state-of-the-art methods. The dataset and development kit are available at https://fomo.norlab.ulaval.ca.

Figures & tables

Figure 1: FoMo dataset is a year-long recording capturing seasonal changes of a typical boreal forest.

Figure 2: The UGV used for the data collection, equipped with tracks on snow (left) and wheels in other seasons (right).

Name & References	Deployment Setting				Sensors					GT
	Environment	Distance	Off-Trail	Off-Pav.	IMU	Mono	Stereo	Lidar	Radar
4Seasons Dataset ( Wenzel et al., 2021 )	Urban	$350\text{\,}\mathrm{km}$								Pose (GNSS)
Oxford RobotCar ( Maddern et al., 2017 )	Urban	$1000\text{\,}\mathrm{km}$								Pose (GNSS)
Boreas Dataset ( Burnett et al., 2023 )	Urban	$350\text{\,}\mathrm{km}$								Pose (GNSS)
FinnForest Dataset ( Ali et al., 2020 )	Forest	$33\text{\,}\mathrm{km}$								Pose (GNSS)
Goose Dataset ( Mortimer et al., 2024 )	Grassland, Woods	$162\text{\,}\mathrm{km}$								Pose (D-GNSS)

Table 1: Overview of the most relevant datasets with deployments spanning multiple seasons. We only mention the GT related to odometry and localization. The label Off-Pav. stands for off-pavement.

Figure 3: Circular view of daily average temperature and snow depth in meters over a year, from November 2024 to November 2025.

Figure 4: An orthomosaic of Forêt Montmorency, the data recording site, with the six repeated trajectories colored by their label. From left to right, we can see Yellow , Red , Blue , Orange , Green , and Magenta . The $\bigstar$ symbol denotes the location of the GNSS base station in the area. The lower-left inset shows the location of Forêt Montmorency within North America.

Figure 5: Eight locations of interest captured in three modalities. The top row shows images captured from the left stereo camera. The second row shows radar scans warped into a Cartesian view. The third row shows point clouds from the RoboSense lidar colored by intensity. The fourth row shows the rear Basler camera rotated by $180\text{\,}\mathrm{\SIUnitSymbolDegree}$ .

Sensor Type	Model	Recording
Wheel encoders	2 $\times$ Hall effect sensors	$4\text{\,}\mathrm{Hz}$
Radar	Navtech CIR-304H	$4\text{\,}\mathrm{Hz}$
Lidar	RoboSense Ruby Plus	$10\text{\,}\mathrm{Hz}$
Lidar	Leishen LS128S1	$10\text{\,}\mathrm{Hz}$
Robot GNSS	3 $\times$ Emlid M2	$10\text{\,}\mathrm{Hz}$
Static GNSS	Emlid Reach RS3	$10\text{\,}\mathrm{Hz}$

Table 2: Sensor specifications ordered by frequencies.

Figure 6: Communication diagram of the FoMo devices. All sensors connected via ETH ( ETH ) are time-synchronized using the IEEE 1588 PTP ( PTP ). The serial link between Emlid Reach Plus and the Neousys computer is used for PPS ( PPS ) signal.

Figure 7: FoMo sensor placement and their coordinate frames. The transformation tree is built with the RoboSense at its root. For simplicity, we omit the right lens of the ZED X camera in this figure.

Figure 8: Snippets of sensor alignment results after extrinsic calibration: Top: RoboSense lidar point cloud (colored by intensity) projected onto the ZED X left camera. Bottom: Leishen lidar points (orange) and RoboSense lidar points (colored by intensity) viewed from the camera perspective. Right: BEV showing the radar image overlaid with extracted radar points (red, colored by intensity) and RoboSense lidar points (colored by intensity).

Figure 9: A translation drift matrix for the Lidar-Inertial Odometry method ( Baril et al., 2022 ) . Each row corresponds to the data used to reconstruct the environment, while each column is the name of the data recording used to localize inside the existing map. Symbol * denotes that the evaluated trajectory covers less than $90\text{\,}\mathrm{\%}$ of the total trajectory duration as reported by the GT .

Method	Nov21	Nov28	Jan29	Mar10	Jun26	Aug20	Sep24	Oct14	Nov03
Proprioceptive ( Madgwick et al., 2011 )	$\mathbf{3.7_{\pm 1.3}}$	$4.6_{\pm 2.1}$	$15.3_{\pm 8.1}$	$4.6_{\pm 1.8}$	$\mathbf{3.9_{\pm 1.4}}$	$\mathbf{3.4_{\pm 1.1}}$	$5.0_{\pm 2.0}$	$3.7_{\pm 1.2}$	$4.6_{\pm 1.6}$
Lidar-Inertial Odometry ( Baril et al., 2022 )	$4.5_{\pm 1.5}$	$4.8_{\pm 1.7}$	$3.8_{\pm 1.5}$	$\mathbf{4.3_{\pm 1.8}}$	$11.7_{\pm 4.8}$	$4.0_{\pm 1.5}$	$25.4{}^{*}_{\pm 13.6}$	$46.5_{\pm 28.3}$	$\mathbf{4.3_{\pm 1.7}}$
RGTR ( Qiao et al., 2025 )	$8.1_{\pm 3.0}$	$14.2_{\pm 6.2}$	$16.8_{\pm 5.9}$	$32.3_{\pm 11.2}$	$8.8_{\pm 3.0}$	$4.2_{\pm 1.5}$	$9.3_{\pm 4.0}$	$24.6_{\pm 10.4}$	$9.3_{\pm 3.5}$
Stereo-Inertial SLAM ( Campos et al., 2021 )	$1.2{}^{*}_{\pm 0.4}$	$\mathbf{1.5_{\pm 0.7}}$	$\mathbf{2.4_{\pm 1.0}}$	$7.6_{\pm 5.4}$	$8.8_{\pm 3.8}$	$1.3{}^{*}_{\pm 0.5}$	$\mathbf{2.3_{\pm 1.0}}$	$\mathbf{0.9_{\pm 0.3}}$	N/A

Table 3: Mean translation drifts for four modalities expressed in percentage [%] along with their standard deviation [

\pm

] using Jan29 for initial map representation. Bold text highlights the best-performing method in each column. Symbol * denotes that the evaluated trajectory covers less than

90\text{\,}\mathrm{\%}

of the total trajectory duration as reported by the GT . N/A is used when the reported trajectory is too short for the evaluation calculation.

Figure 10: Comparison of four methods on the localization task inside an environment reconstruction from Jan21 using data recorded on Oct14 . Due to the presence of high snowbanks during the January environment, both Lidar-Inertial Odometry and Radar-Gyro TaR suffer high localization errors. The proprioceptive sensor does not employ any previous environment reconstruction and serves as a baseline.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 11: A translation drift matrix for the RGTR method ( Qiao et al., 2025 ) . Each row corresponds to the data used to reconstruct the environment, while each column is the name of the deployment used to localize inside the existing map.

Figure 12: A translation drift matrix for ORB-SLAM3 Campos et al. (2021) . Each row corresponds to the data used to reconstruct the environment, while each column is the name of the deployment used to localize inside the existing map. Symbol * denotes that the evaluated trajectory covers less than $90\text{\,}\mathrm{\%}$ of the total trajectory duration as reported by the GT . N/A is used when the reported trajectory is too short for the evaluation calculation.

Date	Trajectory						Drivetrain
	Red	Blue	Green	Magenta	Yellow	Orange
\DTMdate 2024-11-21							Wheels
\DTMdate 2024-11-28							Wheels
\DTMdate 2025-01-10							Tracks
\DTMdate 2025-01-29							Tracks
\DTMdate 2025-03-10							Tracks

Appendix

Table 4: Table of deployments in the FoMo dataset. Trajectory legend: Available, Not available, Alternative route. Conditions: Clear sky, Clouds, Snowfall, Rain, Night.

Figure 14: The forward and backward view from the robot as it drives through key locations across seasons. The locations are separated by white horizontal lines. Time progresses along the horizontal axis with each image in a row taken in different deployments throughout the year. Each column features different locations from the same deployment. N/A indicates a missing trajectory in the deployment. The rear camera auto-exposure failed in the deployment depicted in row 6 column 4.

Date	Ground Truth Quality (Solution % $\|$ # Satellites)
	Red	Blue	Green	Magenta	Yellow	Orange
\DTMdate 2024-11-21	100 \| 19.9	100 \| 18.0	96/3/1 \| 13.2	99/0/1 \| 19.0	99/0/0 \| 18.8	100 \| 19.1
\DTMdate 2024-11-28	100 \| 23.1			99/0/0 \| 21.2	100 \| 20.0
\DTMdate 2025-01-10	100 \| 22.1		75/25/0 \| 15.3	67 /33/0 \| 20.0		100 \| 21.0
\DTMdate 2025-01-29	100 \| 23.9	86 /14/0 \| 22.9	100 \| 21.0	80/20/0 \| 22.5	100 \| 18.9	91/9/0 \| 21.3
\DTMdate 2025-03-10	100 \| 21.8	99/1/0 \| 19.9	100 \| 23.1	100 \| 21.4	100 \| 21.8	98/2/0 \| 22.5

Appendix

Table 5: GNSS GT Statistics (Solution % ( Fixed / Float / Single )

|

Average number of observed satellites). We omit the Float and Single percentages when Fixed is at

100\text{\,}\mathrm{\%}

. indicates that a trajectory is not available. The minimal percentage of Fixed solution and the lowest average number of observed satellites in each column are depicted in bold.

Parameter	Value
Static processing
Output solution	One best
Kinematic processing
Use base observation	On
Shared
Filter type	Combined

Appendix

Table 6: Emlid Studio processing parameters used for PPK positioning. Legend: SNR ( SNR ), IAR ( IAR ) resolves carrier-phase ambiguities. GPS, GLONASS, and BDS are the American, Russian, and Chinese global navigation satellite systems.

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Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads

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Naturalistic Driving Data Lidar Pavement Autonomous Navigation Journey

Open paper AskPDF arXiv

The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autonomous driving algorithms. Boreas-RT comprises 60 sequences collected over 9 real-world routes, totalling 643 km of driving. Each route is traversed multiple times, enabling evaluation in identical environments under varying traffic and, in some cases, weather conditions. The data collection platform includes a 5MP FLIR Blackfly S camera, a 360 degree Navtech RAS6 Doppler-enabled spinning radar, a 128-channel 360 degree Velodyne Alpha Prime lidar, an Aeva Aeries II FMCW Doppler-enabled lidar, a Silicon Sensing DMU41 inertial measurement unit, and a Dynapar wheel encoder. Centimetre-level ground truth is provided via post-processed Applanix POS LV GNSS-INS data. The dataset includes precise extrinsic and intrinsic calibrations, a publicly available development kit, and a live leaderboard for odometry and metric localization. Benchmark results show that many state-of-the-art odometry and localization algorithms overfit to simple driving environments and degrade significantly on the more challenging Boreas-RT routes. Boreas-RT provides a unified dataset for evaluating multi-modal algorithms across diverse road conditions. The dataset, leaderboard, and development kit are available at www.boreas.utias.utoronto.ca.

Date	Ground Truth Quality (Solution % $\|$ # Satellites)
	Red	Blue	Green	Magenta	Yellow	Orange
\DTMdate 2024-11-21	100 \| 19.9	100 \| 18.0	96/3/1 \| 13.2	99/0/1 \| 19.0	99/0/0 \| 18.8	100 \| 19.1
\DTMdate 2024-11-28	100 \| 23.1			99/0/0 \| 21.2	100 \| 20.0
\DTMdate 2025-01-10	100 \| 22.1		75/25/0 \| 15.3	67 /33/0 \| 20.0		100 \| 21.0
\DTMdate 2025-01-29	100 \| 23.9	86 /14/0 \| 22.9	100 \| 21.0	80/20/0 \| 22.5	100 \| 18.9	91/9/0 \| 21.3
\DTMdate 2025-03-10	100 \| 21.8	99/1/0 \| 19.9	100 \| 23.1	100 \| 21.4	100 \| 21.8	98/2/0 \| 22.5

Abstract

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