Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters

Sandro Costa Magalhães, Filipe Neves dos Santos, António Paulo Moreira, Jorge Dias

Abstract

Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D task space?" Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.

MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters

Abstract

Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D task space?" Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.
Paper Structure (11 sections, 10 equations, 18 figures, 2 tables, 1 algorithm)

This paper contains 11 sections, 10 equations, 18 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System and requirements
  3. Materials and Methods
  4. Real data and simulation
  5. Histogram Filter
  6. Camera Projection Model
  7. Objects positioning
  8. Experiments
  9. Results
  10. Discussion
  11. Conclusion

Figures (18)

  • Figure 1: Robot AgRob v16 from INESC TEC to operate under open-field and controlled agricultural environments.
  • Figure 2: Simulated environment to validate the histogram filter effectiveness. Green spheres are the objects being detected, representing the tomatoes, and the black box is the bounding box camera looking at the spheres.
  • Figure 3: Simulated testbed in the laboratory to essay the histogram filter algorithm
  • Figure 4: Intersection between multiple viewpoints in 2D plane
  • Figure 5: Intersection of the camera around in the decomposed space.
  • ...and 13 more figures