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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
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Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: Jul 23, 2025

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
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An embarrassingly simple approach for visual navigation of forest environments.

Chaoyue Niu1, Callum Newlands1, Klaus-Peter Zauner1

  • 1School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.

Frontiers in Robotics and AI
|July 14, 2023
PubMed
Summary

This study introduces a low-cost navigation system for small forest robots using a lightweight neural network to predict terrain depth. The system successfully navigated over 750m of challenging forest terrain in real-world trials.

Keywords:
compliant obstaclesdepth predictionforest simulationlow-cost sensorslow-viewpoint forest navigationoff-road navigationsmall-sized roverssparse swarms

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Area of Science:

  • Field Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Forest navigation presents significant challenges for field robots due to unknown terrains, varied obstacles, and changing environmental conditions.
  • Small, low-viewpoint robots face occlusion issues, unlike larger, more expensive systems typically used in off-road navigation research.
  • Existing navigation systems often rely on costly sensors, limiting accessibility for small-scale robotic applications.

Purpose of the Study:

  • To design and evaluate a low-cost, vision-based navigation system for small-sized rovers in forest environments.
  • To enable autonomous or semi-autonomous navigation of portable robots through complex natural terrains.
  • To address the limitations of low viewpoint and sensor occlusion in small robotic platforms.

Main Methods:

  • A lightweight convolutional neural network (CNN) was employed to predict depth images from low-viewpoint RGB camera input.
  • A coarse-grained navigation algorithm processed predicted depth information to identify traversable paths and avoid obstacles.
  • The system was tested using a Raspberry Pi on a manually pushed mobile platform in both high-fidelity simulations and real-world field trials.

Main Results:

  • The navigation system successfully guided the mobile platform over 750 meters of diverse forest terrain, including shrubs, fallen trees, and uneven ground.
  • Navigation was achieved using minimal input (16x16 depth prediction from 32x32 RGB image) and demonstrated robustness across various weather and lighting conditions.
  • The algorithm showed resilience to camera pitch angle variations, motion blur, low light, and high-contrast lighting.

Conclusions:

  • A low-cost, vision-based navigation system is feasible for small forest rovers, overcoming challenges of low viewpoint and complex terrain.
  • The developed system offers a practical solution for robotic exploration and monitoring in unstructured forest environments.
  • The approach demonstrates the potential for resource-constrained robots to navigate challenging natural landscapes effectively.