Sphere-Graph: A Compact 3D Topological Map for Robotic Navigation and Segmentation of Complex Environments

Topological maps are a common framework for enabling autonomous robotic navigation. To be effective for robotic exploration the maps must be able to be generated quickly and compact enough to store on lightweight hardware. Here we propose a novel 3D topological map called Sphere-Graph which has adaptive edge lengths, can be quickly generated, and can be used to semantically identify hallways and rooms to produce a compact representation of complex environments. We give examples of the Sphere-Graph representation of large 3D urban and cave environments

Handheld spectroradiometer system, computer-readable media, and calibration methods

Non-transitory computer-readable media, spectroradiometer systems, and methods for calibrating a spectroradiometer. In one embodiment, a non-transitory computer-readable medium includes instructions that, when executed by an electronic processor, cause the electronic processor to perform a set of operations. The set of operations includes receiving spectral data regarding an object-of-interest that is captured by a handheld spectroradiometer, detecting a characteristic of the object-of-interest by performing a spectral analysis on the spectral data that is received, and controlling a display to display the characteristic of the object-of-interest.https://digitalcommons.mtu.edu/patents/1154/thumbnail.jp

Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes

Harmful algal blooms (HABs) have become a major health and environmental concern in the Great Lakes. In 2014, severe HABs prompted the State of Ohio to request NASA Glenn Research Center (GRC) to assist with monitoring algal blooms in Lake Erie. The most notable species of HAB is Microcystis aeruginosa, a hepatotoxin producing cyanobacteria that is responsible for liver complications for humans and other fauna that come in contact with these blooms. NASA GRC conducts semiweekly flights in order to gather up-to-date imagery regarding the blooms\u27 spatial extents and concentrations. Airborne hyperspectral imagery is collected using two hyperspectral imagers, HSI-2 and HSI-3. Hyperspectral imagery is necessary in order to conduct experiments on differentiation of algal bloom types based on their spectral reflectance. In this analysis, imagery from September 19, 2016 was utilized to study the subpixel variability within the footprint of arbitrary sized pixels using several analysis techniques. This particular data set is utilized because it represents a worst case scenario where there is significant potential for public health concern due to high concentrations of microcystin toxin found in the water on this day and the concurrent observational challenges to accurately measure the algal bloom concentration variability with a remote sensing system due to the blooms high spatial variability. It has been determined that the optimal spatial resolution to monitor algal blooms in the Great Lakes is at most 50 m, and for much lower error 25 m, thus allowing for greater ease in identifying high concentration blooms near the surface. This resolution provides the best sensitivity to high concentration areas that are of significant importance in regard to human health and ecological damage

Real time HABs mapping using NASA Glenn hyperspectral imager

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015 to 2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 h) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand

A fast algorithm for automatic removal of mirror side banding from MODIS oceancolor imagery

MODIS imagery has long been plagued by optical errors (i.e. banding) introduced by the mirrors on board the spacecraft with a potentially significant impact on scientific study. Banding errors are most evident in the blue channels (412 nm, 440 nm, 490 nm) which are essential for chlorophyll retrieval algorithms. The error parameterization is also influenced by target and environmental factors that make it difficult to fully quantify spatially. The proposed algorithm analyzes a scene using Fourier transformation and computes a spatially dynamic correction factor. Resulting error is typically less than one percent of the underlying signal, tested by artificially introducing banding into a SeaWiFS scene which does not suffer from the mirror side banding problem. Visual analysis also confirms removal of the banding. The algorithm is robust and fast, allowing for batch processing of many scenes without need for manual tuning

Phytoplankton Group Determination using Hyperspectral Remote Sensing in Western Lake Erie

Determination of phytoplankton groups (green algae, diatoms, cryptophytes, and cyanobacteria) in natural water bodies is important information for many stakeholders including scientists, water intake managers, and recreational users. These determinations can be made in the field using fluorescence instrumentation or in the laboratory using microscopy and particle imaging. There is potential to determine phytoplankton groups from remote sensing hyperspectral data measured on the ground, in the air, and from space. Remote sensing provides the unique ability to provide synoptic coverage of a water body without the need to be in the field. These techniques rely on the ability to detect specific spectral absorption features associated with different pigments that vary with phytoplankton group. An extensive data set was collected from May-October 2015 in western Lake Erie that included weekly coincident measurements of phytoplankton composition and water surface reflectance. This robust data set was used to evaluate and generate remote sensing based phytoplankton classification approaches with variable success. Supervised classification methods were able to determine the dominant phytoplankton type from others (cyanobacteria vs. diatoms) while complex machine learning techniques could differentiate types and concentrations

Near real time HABs observations in lake Erie Using a lightweight portable radiometer.

Harmful Algae Blooms (HABs) are an ongoing water quality concern for water treatment management in Lake Erie and involve both remote and direct sensing of water conditions throughout the HABs season. As part of a multi-institutional collaboration with NASA, an inexpensive, Lightweight Portable Radiometer (LPR) system was built using commercial off the shelf parts. This system is used to perform near real time hyperspectral radiance and irradiance measurements at a remote location accessible only by boat in the Western Basin of Lake Erie. Sampling frequency is on sixty second intervals for up to several hours per day, daily, for a period of over three months. Observations from this system reveal daily and seasonal variations in atmospheric and water quality conditions, including highly local HABs surface scum

Bio-optical retrieval algorithm for the optically shallow waters of the Great Lakes

With the exception of a few areas, Lake Michigan is an oligotrophic clear water body. It is predominantly in the nearshore where ecology-relevant processes unfold due to natural and anthropogenic forcing. However, the bottom influence is strong enough to contaminate satellite observed signal, thus impeding the remote sensing of water quality parameters within the coastal zone. A new approach based on a radiative transfer model, a specific hydro-optical model and multivariate optimization has been developed to produce a tool for operational satellite retrievals of water quality parameters in optically shallow areas. The algorithm retrieves concentrations of the Color Producing Agents (CPAs), chlorophyll, suspended matter, and CDOM in coastal waters with varying bottom types. The sensitivity of the new approach was tested for hydro-optical conditions in Lake Michigan. MODIS data acquisitions were synchronized with in situ radiometric measurements, as well as identification of bottom type and depth. Retrievals of remote sensing reflectance and CPA concentrations within the ranges of depth where bottom reflectance is detectable compared will with in situ observations. Application of the developed operational tool has convincingly shown its advantage over the OC4 performance in optically shallow waters at all control stations

High resolution satellite-based water depth mapping in the Great Lakes

The capability to map water depth using satellite imagery can help fulfill bathymetric mapping needs in nearshore regions, especially where other sources such as LiDAR and sonar have not been able to reach all areas. The ability to accurately map water depth with satellite imagery lessens the need for expensive field work to derive bathymetry. Using high spatial resolution commercial satellite imagery to map depth can provide bathymetry data with accuracies better than one half meter. Several satellite depth mapping methods exist and have been tested to determine their accuracies and limitations in the Sleeping Bear Dunes National Lakeshore (SBDNL) nearshore area. Traditional algorithms required several inputs to calculate depth that may not be readily available or have acceptable accuracy. A new technique has been developed that requires less ancillary input data than existing algorithms by deriving them directly from the image being processed. Accuracies of this new technique, when compared to coastal bathymetric LiDAR, are presented for the SBDNL which primary bottom types consist of sand and submerged aquatic vegetation (SAV). The new technique was also evaluated using several different high-resolution commercial satellite sensors, including WorldView-2 and GeoEye-1

Water quality observations in the Great Lakes using an optimized satellite bio-optical algorithm

The Color Producing Agent Algorithm (CPA-A) is a semi-analytical inverse radiative transfer bio-optical model to retrieve water quality parameters from satellite observed reflectance. The CPA-A requires knowledge of the inherent optical properties of a given water body to produce accurate retrievals of the primary color producing agents (CPA) namely chlorophyll (CHL), suspended matter (SM), and CDOM. An optimized set of inherent optical properties, known as a hydro-optical (HO) model, has been generated for Lakes Michigan, Superior, and Huron that produce robust retrievals annually and intraannually for the MODIS mission (2002-2013). The optimized HO model was used to generate long term time series estimates of several water quality parameters including CHL, SM, CDOM, DOC, attenuation, absorption, backscatter, and photic depth. The diffuse attenuation coefficient (Kd) and photic depth are functions of CPA concentration and are therefore inherently retrievable with the CPA-A. Retrieved concentrations of CPA-A derived water quality parameters compare favorably with in situ measurements in the upper three Lakes. This complete set of water quality parameters provides unique observations of the lower food web including primary production to help better understand ecological changes due to anthropogenic forcing and climate change