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

Peize Li; Kaiwen Cai; Muhamad Risqi U. Saputra; Zhuangzhuang Dai; Chris Xiaoxuan Lu

doi:10.1109/IROS47612.2022.9981865

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

Peize Li, Kaiwen Cai, Muhamad Risqi U. Saputra, Zhuangzhuang Dai, Chris Xiaoxuan Lu

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher	IEEE
Number of pages	6
DOIs	https://doi.org/10.1109/IROS47612.2022.9981865
Publication status	Published - 23 Oct 2022

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10.1109/IROS47612.2022.9981865

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@inproceedings{15414fe2e1b444cb931cac5305ccc019,

title = "OdomBeyondVision:: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum",

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.",

author = "Peize Li and Kaiwen Cai and Saputra, {Muhamad Risqi U.} and Zhuangzhuang Dai and Lu, {Chris Xiaoxuan}",

year = "2022",

month = oct,

day = "23",

doi = "10.1109/IROS47612.2022.9981865",

language = "English",

booktitle = "2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",

publisher = "IEEE",

address = "United States",

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T1 - OdomBeyondVision:

T2 - An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum

AU - Li, Peize

AU - Cai, Kaiwen

AU - Saputra, Muhamad Risqi U.

AU - Dai, Zhuangzhuang

AU - Lu, Chris Xiaoxuan

PY - 2022/10/23

Y1 - 2022/10/23

N2 - 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.

AB - 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.

UR - https://ieeexplore.ieee.org/abstract/document/9981865

U2 - 10.1109/IROS47612.2022.9981865

DO - 10.1109/IROS47612.2022.9981865

M3 - Conference publication

BT - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

PB - IEEE

ER -

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

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