29 Sep 2023

Low Size, Weight, Power, and Cost (SWaP-C) Payload for Autonomous Navigation and Mapping on an Unmanned Ground Vehicle

Abstract: Autonomous navigation and unknown environment exploration with an unmanned ground vehicle (UGV) is extremely challenging. This report investigates a mapping and exploration solution utilizing low size, weight, power, and cost payloads. The platform presented here leverages simultaneous localization and mapping to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, 3D lidar, and red-green-blue and depth cameras. The main goal of this effort is to leverage path planning and navigation for mapping and exploration with a UGV to produce an accurate 3D map. The solution provided also leverages the Robot Operating System

12 Sep 2023

UGV SLAM Payload for Low-Visibility Environments

Abstract: Herein, we explore using a low size, weight, power, and cost unmanned ground vehicle payload designed specifically for low-visibility environments. The proposed payload simultaneously localizes and maps in GPS-denied environments via waypoint navigation. This solution utilizes a diverse sensor payload that includes wheel encoders, inertial measurement unit, 3D lidar, 3D ultrasonic sensors, and thermal cameras. Furthermore, the resulting 3D point cloud was compared against a survey-grade lidar.

20 Jun 2023

3D Mapping and Navigation Using MOVEit

Abstract: Until recently, our focus has been primarily on the development of a low SWAP-C payload for deployment on a UGV that leverages 2D mapping and navigation. Due to these efforts, we are able to autonomously map and navigate very well within flat indoor environments. This report will explore the implementation of 3D mapping and navigation to allow unmanned vehicles to operate on a variety of terrains, both indoor and outdoor. The method we followed uses MOVEit, a motion planning framework. The MOVEit application is typically used in the control of robotic arms or manipulators, but its handling of 3D perception using OctoMaps makes it a promising software for robots in general. The challenges of using MOVEit outside of its intended use case of manipulators are discussed in this report.

30 Sep 2022

Buried-Object-Detection Improvements Incorporating Environmental Phenomenology into Signature Physics

Abstract: The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environmental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.

15 Aug 2022

Thermal Infra-Red Comparison Study of Buried Objects between Humid and Desert Test Beds

Abstract: This study pertains to the thermal variations caused by buried objects and their ramifications on soil phenomenology. A multitude of environmental conditions were investigated to observe the effect on thermal infrared sensor performance and detection capabilities. Correlations between these external variables and sensor contrast metrics enable determinable key factors responsible for sensor degradation. This document consists of two parts. The first part is a summary of data collected by the U.S. Army Corps of Engineers, Engineer and Research and Development Center Cold Regions Research and Engineering Laboratory (ERDC-CRREL), ERDC-Geotechnical Structures Laboratory, and Desert Research Institute at the Yuma Proving Ground (YPG) site in February 2020 and observations from this activity. The second part is a comparison of target visibility between data collected at YPG and data collected at the ERDC-CRREL test site in 2018.

2 Mar 2022

Modernizing Environmental Signature Physics for Target Detection—Phase 3

Abstract: The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

22 Jul 2021

Modernizing Environmental Signature Physics for Target Detection

Abstract: The objective of this study was to determine the effect of environmental phenomonology on the ability to detect buried objects and to provide a predictive capability of when targets are best detectable with IR sensors. Jay Clausen presented this material at the ERDC RD20 Conference.

20 Aug 2020

PUBLICATION NOTICE: Spatial and Temporal Variance in the Thermal Response of Buried Objects

ABSTRACT:  Probability of detection and false alarm rates for current military sensor systems used for detecting buried objects are often unacceptable. One approach to increasing sensor performance and detection reliability is to better understand which physical processes are dominant under certain environmental conditions. Incorporating this understanding into detection algorithms will improve detection performance. Our approach involved studying a small, 3.05 × 3.05 m, test plot at the Engineer Research and Development Center’s Cold Regions Research and Engineering Laboratory (ERDC-CRREL) in Hanover, New Hampshire. There we monitored a number of environmental variables (soil temperature moisture, and chemistry as well as air temperature and humidity, cloud cover, and incoming solar radiation) coupled with thermal infrared and electro-optical image collection. Data collection occurred over 4 months with measurements made at 15 minute intervals. Initial findings show that significant spatial and thermal temporal variability is caused by incoming solar radiation; meteorologically driven surface heat exchange; and subsurface-soil temperatures, density, moisture content, and surface roughness.

2 Jun 2020

PUBLICATION NOTICE: An Assessment of an Inexpensive COTS 360-degree Camera for Mapping and Localization in a GPS-denied Area

Purpose: The efficacy of using a low Size, Weight, Power, and Cost (SWAP-C) Commercial off the Shelf (COTS) 360-degree camera was evaluated for localization and positioning in a GPS-denied environment. Specifically, OpenVSLAM was utilized to generate point clouds with negligible drift using a RICOH THETA S 360-degree field of view camera without the aid of an Inertial Measurement Unit (IMU). OpenVSLAM is also demonstrated to show that it can localize and position with a pre-generated map.

23 Apr 2020

PUBLICATION NOTICE: Optimized Low Size, Weight, Power and Cost (SWaP-C) Payload for Mapping Interiors and Subterranean on an Unmanned Ground Vehicle

ABSTRACT: Section 3 of the FY15 Force 2025 Maneuvers Annual Report indicates that in Dense Urban Areas (DUA), specifically in a subsurface, surface, or super-surface structure, the ability to identify threats will be diminished. Most commercially available LIght Detection And Ranging (LIDAR) systems are specifically designed for high-resolution aerial imaging and mapping applications. As a result, they tend to be large, heavy, power-hungry, data bandwidth intensive, and expensive. They also employ lasers that are not typically eye-safe, which limits their overall effectiveness in subterranean and the interiors of subsurface or super-surface structures. However, due to recent advances in the automotive industry, there are new generations of Size, Weight, Power, and Cost (SWaP-C) sensors that are eye-safe, making them suitable for use indoors and in subterranean environments. While these tradeoffs limit their effective use to hundreds of meters (compared to kilometers for their more expensive counterparts), they are ideal candidates for use in subterranean and building interiors. While cameras fill this niche to some extent, the volumetric calculations provided by these sensors provide additional intelligence to shape the security of the environment and offer more precision when maneuvering troops. These sensors would provide the warfighter with situational understanding in previously inaccessible locations. Therefore, to aid in the Army’s need to obtain and maintain situational understanding in DUAs, the authors propose utilizing low size, weight, power, and cost (SWaP-C) sensors, on a robot platform, for surveying and mapping underground structures and building interiors. Rapid/near real-time data processing is possible by utilizing open-source software and commercial off the shelf (COTS) components. Using the preferred sensor payload autonomously was also explored.