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Extrinsic Calibration and Verification of Multiple Non-overlapping Field of View Lidar Sensors
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap. Scania CV AB, Södertälje, Sweden..
Scania CV AB, Södertälje, Sweden..
Scania CV AB, Södertälje, Sweden..
Scania CV AB, Södertälje, Sweden..
Vise andre og tillknytning
2022 (engelsk)Inngår i: 2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022), Institute of Electrical and Electronics Engineers (IEEE) , 2022Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We demonstrate a multi-lidar calibration framework for large mobile platforms that jointly calibrate the extrinsic parameters of non-overlapping Field-of-View (FoV) lidar sensors, without the need for any external calibration aid. The method starts by estimating the pose of each lidar in its corresponding sensor frame in between subsequent timestamps. Since the pose estimates from the lidars are not necessarily synchronous, we first align the poses using a Dual Quaternion (DQ) based Screw Linear Interpolation. Afterward, a HandEye based calibration problem is solved using the DQ-based formulation to recover the extrinsics. Furthermore, we verify the extrinsics by matching chosen lidar semantic features, obtained by projecting the lidar data into the camera perspective after time alignment using vehicle kinematics. Experimental results on the data collected from a Scania vehicle [similar to 1 Km sequence] demonstrate the ability of our approach to obtain better calibration parameters than the provided vehicle CAD model calibration parameters. This setup can also be scaled to any combination of multiple lidars.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE) , 2022.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-326490DOI: 10.1109/ICRA46639.2022.9811704ISI: 000941265700085Scopus ID: 2-s2.0-85136321668OAI: oai:DiVA.org:kth-326490DiVA, id: diva2:1754404
Konferanse
IEEE International Conference on Robotics and Automation (ICRA), MAY 23-27, 2022, Philadelphia, PA, USA
Merknad

QC 20230626

Tilgjengelig fra: 2023-05-03 Laget: 2023-05-03 Sist oppdatert: 2024-02-13bibliografisk kontrollert
Inngår i avhandling
1. State estimation with auto-calibrated sensor setup
Åpne denne publikasjonen i ny fane eller vindu >>State estimation with auto-calibrated sensor setup
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Localization and mapping is one of the key aspects of driving autonomously in unstructured environments. Often such vehicles are equipped with multiple sensor modalities to create a 360o sensing coverage and add redundancy to handle sensor dropout scenarios. As the vehicles operate in underground mining and dense urban environments the Global navigation satellite system (GNSS) is often unreliable. Hence, to create a robust localization system different sensor modalities like camera, lidar and IMU are used along with a GNSS solution. The system must handle sensor dropouts and work in real-time (~15 Hz), so that there is enough computation budget left for other tasks like planning and control. Additionally, precise localization is also needed to map the environment, which may be later used for re-localization of the autonomous vehicles as well. Finally, for all of these to work seamlessly, accurate calibration of the sensors is of utmost importance.

In this PhD thesis, first, a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data is presented. State estimation was performed in real-time, which produced robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS components using a factor graph framework. The proposed method handled signal loss with a novel synchronization and fusion mechanism. To validate the approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ~ 7 Km). An average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair is reported.  

Since precise calibration is needed for the localization and mapping tasks, in this thesis, methods for real-time calibration of the sensor setup is proposed. First, a method is proposed to calibrate sensors with non-overlapping field-of-view. The calibration quality is verified by mapping known features in the environment. Nevertheless, the verification process was not real-time and no observability analysis was performed which could give us an indicator of the analytical traceability of the trajectory required for motion-based online calibration. Hence, a new method is proposed where calibration and verification were performed in real-time by matching estimated sensor poses in real-time with observability analysis. Both of these methods relied on estimating the sensor poses using the state estimator developed in our earlier works. However, state estimators have inherent drifts and they are computationally intensive as well. Thus, another novel method is developed where the sensors could be calibrated in real-time without the need for any state estimation. 

sted, utgiver, år, opplag, sider
KTH Royal Institute of Technology, 2024. s. 151
Serie
TRITA-EECS-AVL ; 2024:8
Emneord
SLAM, Sensor calibration, Autonomous driving
HSV kategori
Forskningsprogram
Elektro- och systemteknik
Identifikatorer
urn:nbn:se:kth:diva-343412 (URN)978-91-8040-806-6 (ISBN)
Disputas
2024-03-08, https://kth-se.zoom.us/s/63372097801, F3, Lindstedtsvägen 26, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Foundation for Strategic Research
Merknad

QC 20240213

Tilgjengelig fra: 2024-02-14 Laget: 2024-02-12 Sist oppdatert: 2024-03-07bibliografisk kontrollert

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