OdoViz: A 3D Odometry Visualization and Processing Tool

TL;DR

OdoViz is a versatile, web-based 3D visualization tool for autonomous vehicle datasets that simplifies data inspection, processing, and comparison, supporting research and customization with an open-source platform.

Contribution

It introduces a flexible, web-based system for visualizing and processing autonomous vehicle data, with support for multiple datasets, deployment options, and extensibility through plugins.

Findings

Supports multiple datasets and custom data loading

Enables simultaneous visualization of multiple trajectories

Reduces effort in dataset sub-sampling and comparison

Abstract

OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets designed to support common tasks in visual place recognition research. The system includes functionality for loading, inspecting, visualizing, and processing GPS/INS poses, point clouds and camera images. It supports a number of commonly used driving datasets and can be adapted to load custom datasets with minimal effort. OdoViz's design consists of a slim server to serve the datasets coupled with a rich client frontend. This design supports multiple deployment configurations including single user stand-alone installations, research group installations serving datasets internally across a lab, or publicly accessible web-frontends for providing online interfaces for exploring and interacting with datasets. The tool allows viewing complete vehicle trajectories traversed at multiple different time periods simultaneously, facilitating tasks such as sub-sampling, comparing and finding pose correspondences both across and within sequences. This significantly reduces the effort required in creating subsets of data from existing datasets for machine learning tasks. Further to the above, the system also supports adding custom extensions and plugins to extend the capabilities of the software for other potential data management, visualization and processing tasks. The platform has been open-sourced to promote its use and encourage further contributions from the research community.