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

TL;DR

This paper presents a stereo-vision system integrated with SLAM to improve object detection and size estimation, especially for non-tangible objects, enhancing autonomous robot navigation.

Contribution

It introduces a stereo-vision based approach combined with machine learning to detect and estimate non-tangible objects, complementing LiDAR in SLAM systems.

Findings

High accuracy in object depth and size estimation demonstrated

Effective detection of non-tangible objects like smoke and steam

Enhanced navigation capabilities in complex environments

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}.