Ian Q. Mattson

Ian Q. Mattson

he/him

PhD Student at Michigan Tech

Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

Red vs. Infrared: comparing 900nm and 1550nm Lidar performance in arctic winter conditions

Published in SPIE Optics and Photonics, Laser Communication and Propagation through the Atmosphere and Oceans XIV, 2025

We present here the results of a data collection campaign involving a 1550 nm and 900 nm lidar operating in severe winter weather conditions. Data for both lidars was collected simultaneously during arctic-like conditions where fine snow or ice particles are often suspended in the air severely reducing visibility. In this work we qualitatively and quantitatively compare the performance of these lidars. Performance is compared with respect to clutter noise from scattering, object detection in the presence of snow. We find the 1550 nm lidar is more likely to detect snow, resulting in increased clutter noise.

Recommended citation: I. Q. Mattson, L. Schexnaydre, and J. P. Bos, “Red vs infrared: Comparing 900nm and 1550nm lidar performance in Arctic Winter Conditions,” Laser Communication and Propagation through the Atmosphere and Oceans XIV, p. 1, Sep. 2025. doi:10.1117/12.3064732
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Rut depth detection for automated trafficability assessment

Published in SPIE Defense + Commercial Sensing, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, 2023

The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles.

Recommended citation: I. Q. Mattson, Z. D. Jeffries, C. D. Majhor, and J. P. Bos, “Rut depth detection for automated trafficability assessment,” Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, p. 26, Jun. 2023. doi:10.1117/12.2664429
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Reducing ego vehicle energy-use by LiDAR-based lane-level positioning

Published in SPIE Defense + Commercial Sensing, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, 2022

In addition to providing convenience and improving safety autonomous vehicle technologies offer an opportunity to reduce energy use by up to twenty percent or more. One strategy for reducing energy use is careful positioning of an autonomous vehicle, the ego vehicle, behind one or more lead vehicles. Most perception pipelines fit a bounding box around the center of mass of a detected object. That approach may not be accurate enough to allow for precise positioning. Here we compare different methods of identifying vehicle boundaries and vehicle type using a combination of simulation and field testing. Approaches will be compared based on required LiDAR resolution and algorithm complexity relative to potential improvement in energy efficiency.

Recommended citation: I.Q. Mattson et al., “Reducing ego vehicle energy-use by LIDAR-based Lane-level positioning,” Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, p. 2, Jun. 2022. doi:10.1117/12.2619430
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