Algorithmically Improved Framework for Image-only Robotic Mapping

System design for inferring colony-level pollination activity through miniature bee-mounted sensors

Scientific Reports ◽

10.1038/s41598-021-82537-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Haron M. Abdel-Raziq ◽

Daniel M. Palmer ◽

Phoebe A. Koenig ◽

Alyosha C. Molnar ◽

Kirstin H. Petersen

Keyword(s):

Data Acquisition ◽

Honey Bees ◽

Large Scale ◽

Physical World ◽

Cluttered Environments ◽

Robotic Mapping ◽

Large Scale Data ◽

Trade Offs ◽

Large Numbers ◽

Engineered Systems

AbstractIn digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.

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Multiellipsoidal Mapping Algorithm

Applied Sciences ◽

10.3390/app8081239 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1239 ◽

Cited By ~ 2

Author(s):

Carlos Villaseñor ◽

Nancy Arana-Daniel ◽

Alma Alanis ◽

Carlos Lopez-Franco ◽

Javier Gomez-Avila

Keyword(s):

Point Clouds ◽

Mapping Algorithm ◽

Robotic Mapping ◽

Wide Range ◽

Important Challenge ◽

Memory Cost ◽

Environmental Representation ◽

Object Shapes ◽

Quadratic Surfaces ◽

Object Mapping

The robotic mapping problem, which consists in providing a spatial model of the environment to a robot, is a research topic with a wide range of applications. One important challenge of this problem is to obtain a map that is information-rich (i.e., a map that preserves main structures of the environment and object shapes) yet still has a low memory cost. Point clouds offer a highly descriptive and information-rich environmental representation; accordingly, many algorithms have been developed to approximate point clouds and lower the memory cost. In recent years, approaches using basic and “simple” (i.e., using only planes or spheres) geometric entities for approximating point clouds have been shown to provide accurate representations at low memory cost. However, a better approximation can be implemented if more complex geometric entities are used. In the present paper, a new object-mapping algorithm is introduced for approximating point clouds with multiple ellipsoids and other quadratic surfaces. We show that this algorithm creates maps that are rich in information yet low in memory cost and have features suitable for other robotics problems such as navigation and pose estimation.

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