OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum

TL;DR

This paper introduces OdomBeyondVision, a comprehensive multimodal indoor odometry dataset with diverse sensors and platforms, enabling advanced research in indoor navigation across different spectra and conditions.

Contribution

The paper provides a novel, publicly available multimodal odometry dataset with diverse sensors and platforms, including emerging sensors like mmWave radar and LWIR thermal cameras.

Findings

Includes data from multiple sensors and platforms in various indoor scenes.

Provides baseline odometry implementations for future comparison.

Enables research across visible and non-visible spectra for indoor navigation.

Abstract

This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.