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Object Depth and Size Estimation using Stereo-vision and Integration with SLAM

Layth Hamad, Muhammad Asif Khan, Amr Mohamed

TL;DR

This letter proposes a highly accurate stereo-vision approach to complement LiDAR in autonomous robots that employs advanced stereo vision-based object detection to detect both tangible and nontangible objects and then uses simple machine learning to precisely estimate the depth and size of the object.

Abstract

Autonomous robots use simultaneous localization and mapping (SLAM) for efficient and safe navigation in various environments. LiDAR sensors are integral in these systems for object identification and localization. However, LiDAR systems though effective in detecting solid objects (e.g., trash bin, bottle, etc.), encounter limitations in identifying semitransparent or non-tangible objects (e.g., fire, smoke, steam, etc.) due to poor reflecting characteristics. Additionally, LiDAR also fails to detect features such as navigation signs and often struggles to detect certain hazardous materials that lack a distinct surface for effective laser reflection. In this paper, we propose a highly accurate stereo-vision approach to complement LiDAR in autonomous robots. The system employs advanced stereo vision-based object detection to detect both tangible and non-tangible objects and then uses simple machine learning to precisely estimate the depth and size of the object. The depth and size information is then integrated into the SLAM process to enhance the robot's navigation capabilities in complex environments. Our evaluation, conducted on an autonomous robot equipped with LiDAR and stereo-vision systems demonstrates high accuracy in the estimation of an object's depth and size. A video illustration of the proposed scheme is available at: \url{https://www.youtube.com/watch?v=nusI6tA9eSk}.

Object Depth and Size Estimation using Stereo-vision and Integration with SLAM

TL;DR

This letter proposes a highly accurate stereo-vision approach to complement LiDAR in autonomous robots that employs advanced stereo vision-based object detection to detect both tangible and nontangible objects and then uses simple machine learning to precisely estimate the depth and size of the object.

Abstract

Autonomous robots use simultaneous localization and mapping (SLAM) for efficient and safe navigation in various environments. LiDAR sensors are integral in these systems for object identification and localization. However, LiDAR systems though effective in detecting solid objects (e.g., trash bin, bottle, etc.), encounter limitations in identifying semitransparent or non-tangible objects (e.g., fire, smoke, steam, etc.) due to poor reflecting characteristics. Additionally, LiDAR also fails to detect features such as navigation signs and often struggles to detect certain hazardous materials that lack a distinct surface for effective laser reflection. In this paper, we propose a highly accurate stereo-vision approach to complement LiDAR in autonomous robots. The system employs advanced stereo vision-based object detection to detect both tangible and non-tangible objects and then uses simple machine learning to precisely estimate the depth and size of the object. The depth and size information is then integrated into the SLAM process to enhance the robot's navigation capabilities in complex environments. Our evaluation, conducted on an autonomous robot equipped with LiDAR and stereo-vision systems demonstrates high accuracy in the estimation of an object's depth and size. A video illustration of the proposed scheme is available at: \url{https://www.youtube.com/watch?v=nusI6tA9eSk}.
Paper Structure (11 sections, 3 equations, 4 figures)

This paper contains 11 sections, 3 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Framework
  4. Obstacle Detection
  5. Depth Estimation
  6. Dimension Estimation
  7. Integration with SLAM
  8. Experiments and Results
  9. Experiments
  10. Evaluation and Results
  11. Conclusion

Figures (4)

  • Figure 1: Stereo vision image with object detection from the two cameras of the robot showing the disparity of the objects.
  • Figure 2: The proposed framework for estimating object depth (distance) and dimensions (size in the real world).
  • Figure 3: Evaluation of the proposed stereo-vision framework for obstacle avoidance.
  • Figure 4: Integration of the proposed method in SLAM for object avoidance and navigation.