An indoor DSO-based ceiling-vision odometry system for indoor industrial environments

TL;DR

This paper presents Ceiling-DSO, a novel ceiling-vision odometry system based on Direct Sparse Odometry, designed for indoor industrial environments, capable of robust localization without relying on specific ceiling features.

Contribution

The paper introduces Ceiling-DSO, a versatile ceiling-vision odometry method that avoids assumptions about ceiling features, and provides a new dataset for evaluation in real-world industrial scenarios.

Findings

Ceiling-DSO achieves acceptable error rates in pose estimation.

The system is applicable to various ceiling types without specific landmarks.

A new dataset was created for ceiling-vision evaluation.

Abstract

Autonomous Mobile Robots operating in indoor industrial environments require a localization system that is reliable and robust. While Visual Odometry (VO) can offer a reasonable estimation of the robot's state, traditional VO methods encounter challenges when confronted with dynamic objects in the scene. Alternatively, an upward-facing camera can be utilized to track the robot's movement relative to the ceiling, which represents a static and consistent space. We introduce in this paper Ceiling-DSO, a ceiling-vision system based on Direct Sparse Odometry (DSO). Unlike other ceiling-vision systems, Ceiling-DSO takes advantage of the versatile formulation of DSO, avoiding assumptions about observable shapes or landmarks on the ceiling. This approach ensures the method's applicability to various ceiling types. Since no publicly available dataset for ceiling-vision exists, we created a custom dataset in a real-world scenario and employed it to evaluate our approach. By adjusting DSO parameters, we identified the optimal fit for online pose estimation, resulting in acceptable error rates compared to ground truth. We provide in this paper a qualitative and quantitative analysis of the obtained results.