Field Report on a Wearable and Versatile Solution for Field Acquisition and Exploration

TL;DR

This work presents a wearable, plug-and-play data acquisition backpack designed for field deployment, enabling a single operator to record images, lidar, IMU, and GNSS data in rugged environments. The authors detail the three-part platform (backpack, sensors, control panel), the hardware choices (Pelican Case, Jetson Xavier AGX, Basler cameras, VLP-16, Xsens IMU, Reach GNSS), and the software workflow, including a 10 Gbps network topology and hardware-triggered synchronization. Performance metrics report ~872 Mbps from cameras and ~8 Mbps from LiDAR, about 79 W power draw and ~5.5 CPU cores used, indicating a capable but energy-limited system. Lessons learned cover bandwidth management, user interface benefits, and camera overheating mitigation, while use cases demonstrate BorealHDR data collection and teach-and-repeat with autonomous robots, illustrating practical impact for forestry and industrial data gathering. The work is complemented by open-source CAD files and software on GitHub, enabling adoption and customization.

Abstract

This report presents a wearable plug-and-play platform for data acquisition in the field. The platform, extending a waterproof Pelican Case into a 20 kg backpack offers 5.5 hours of power autonomy, while recording data with two cameras, a lidar, an Inertial Measurement Unit (IMU), and a Global Navigation Satellite System (GNSS) receiver. The system only requires a single operator and is readily controlled with a built-in screen and buttons. Due to its small footprint, it offers greater flexibility than large vehicles typically deployed in off-trail environments. We describe the platform's design, detailing the mechanical parts, electrical components, and software stack. We explain the system's limitations, drawing from its extensive deployment spanning over 20 kilometers of trajectories across various seasons, environments, and weather conditions. We derive valuable lessons learned from these deployments and present several possible applications for the system. The possible use cases consider not only academic research but also insights from consultations with our industrial partners. The mechanical design including all CAD files, as well as the software stack, are publicly available at https://github.com/norlab-ulaval/backpack_workspace.

Figures (6)

• Figure 1: Picture of the developed backpack. Main components are identified as follows: (1) Two Basler a2A1920-51gcPRO cameras, (2) Xsens MTI-30 IMU, (3) VLP16 3D lidar, (4) Emlid Reach RS+ GPS receiver, (5) Ubiquiti UniFi UAP-AC-M Wi-Fi antenna, (6) visualization tablet, and (7) control panel.
• Figure 2: Block diagram of the hardware components of the mobile data acquisition system. Black lines show electrical connections, purple lines stand for Ethernet links and green lines are USB or serial cables.
• Figure 3: Picture of the inside of the backpack. Main components are identified as follows: (1) TRENDnet TEG-S762 switch, (2) EGO battery 2.5Ah, (3) STM32F407 microcontroller, (4) Nvidia Jetson Xavier AGX Developer Kit, and (5) Asus XG-C100C 10G bs PCIe.
• Figure 4: Examples of environments traveled with the acquisition platform. (A) Winter displacement on a snowmobile, (B) Winter frozen lake, (C) Winter dense forest, (D) Winter tree corridor, (E) Spring muddy forest, and (F) Summer forest.
• Figure 5: Satellite image of the Montmorency Forest, highlighting all the trajectories traveled on a one-day span in winter. The purple lines are the GNSS positions from the 29.0 recorded trajectories, while the white arrows point to the roads traveled with the snowmobile. The backpack recording platform is attached to the end of the snowmobile only for the displacement between regions.