Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Tschopp, Florian; Schneider, Thomas; Palmer, A. W.; Nourani-Vatani, Navid; Cadena, César; Siegwart, Roland; Nieto, Juan

doi:10.1109/lra.2019.2897169

Cited by 34 publications

(33 citation statements)

References 44 publications

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“…Furthermore, due to the constraint motion of the vehicle and low signal to noise ratio (SNR), the IMU should be of a high quality and also should be temperature calibrated Table III. The results obtained with VersaVIS (details found in our previous work [5]) show that by using stereo cameras and tightly synchronized IMU measurements we can improve robustness and provide accurate odometry up to 1.11 % of the travelled distance on railway scenarios and speeds up to 52.4 km /h.…”

Section: B Stereo Visual-inertial Odometry On Rail Vehiclementioning

confidence: 91%

“…The need for public transportation is heavily increasing while current infrastructure is reaching its limits. Using onboard sensors with reliable and accurate positioning, the efficiency of the infrastructure could be highly increased [5]. However, this requires the fusion of multiple independent positioning modalities of which one could be visual-aided odometry.…”

Section: B Stereo Visual-inertial Odometry On Rail Vehiclementioning

confidence: 99%

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VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite

Tschopp

Riner

Bernreiter

et al. 2020

Sensors

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Robust and accurate pose estimation is crucial for many applications in mobile robotics. Extending visual Simultaneous Localization and Mapping (SLAM) with other modalities such as an inertial measurement unit (IMU) can boost robustness and accuracy. However, for a tight sensor fusion, accurate time synchronization of the sensors is often crucial. Changing exposure times, internal sensor filtering, multiple clock sources and unpredictable delays from operation system scheduling and data transfer can make sensor synchronization challenging. In this paper, we present VersaVIS, an Open Versatile Multi-Camera Visual-Inertial Sensor Suite aimed to be an efficient research platform for easy deployment, integration and extension for many mobile robotic applications. VersaVIS provides a complete, open-source hardware, firmware and software bundle to perform time synchronization of multiple cameras with an IMU featuring exposure compensation, host clock translation and independent and stereo camera triggering. The sensor suite supports a wide range of cameras and IMUs to match the requirements of the application. The synchronization accuracy of the framework is evaluated on multiple experiments achieving timing accuracy of less than 1 ms. Furthermore, the applicability and versatility of the sensor suite is demonstrated in multiple applications including visual-inertial SLAM, multi-camera applications, multimodal mapping, reconstruction and object based mapping.

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Section: B Stereo Visual-inertial Odometry On Rail Vehiclementioning

confidence: 91%

Section: B Stereo Visual-inertial Odometry On Rail Vehiclementioning

confidence: 99%

VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite

Tschopp

Riner

Bernreiter

et al. 2020

Sensors

Self Cite

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“…We now need to assess the evolution of state variables (8). The evolution of R IMU n , p IMU n , v IMU n , b ω n and b a n is already given by the standard equations (3)- (7).…”

Section: A Defining the Dynamical Model F (·)mentioning

confidence: 99%

“…One must define a linearized error, an underlying group to derive the exponential map, and then methodology is akin to the EKF's. 1) Linearized Error: the filter state x n is given by (8). The state evolution is given by the dynamics (5)- (7) and (12)- (13), see Section II.…”

Section: Appendix Amentioning

confidence: 99%

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AI-IMU Dead-Reckoning

Brossard

Barrau

Bonnabel

2020

IEEE Trans. Intell. Veh.

183

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In this paper we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometry dataset, and our dead-reckoning inertial method based only on the IMU accurately estimates 3D position, velocity, orientation of the vehicle and self-calibrates the IMU biases. We achieve on average a 1.10% translational error and the algorithm competes with top-ranked methods which, by contrast, use LiDAR or stereo vision. We make our implementation open-source at: https://github.com/mbrossar/ai-imu-drIndex Terms-localization, deep learning, invariant extended Kalman filter, KITTI dataset, inertial navigation, inertial measurement unit

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Four years of multimodal odometry and mapping on the rail vehicles

Wang,

Song,

Zhang

et al. 2023

Journal of Field Robotics

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Precise, seamless, and efficient train localization as well as long‐term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety engineering for railroad systems. Simultaneous localization and mapping is right at the core of solving the two problems concurrently. To this end, we propose a high‐performance and versatile multimodal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial‐centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation, and map‐based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors inertial measurement unit and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared with point‐only LiDAR‐inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The visual‐inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long‐during railway environments over 4 years, including general‐speed, high‐speed, and metro, where both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experiences, problems, and successes of our group with the robotics community so that those who work in such environments can avoid these errors. In this view, we open source some of the data sets to benefit the research community.

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Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Cited by 34 publications

References 44 publications

VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite

VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite

AI-IMU Dead-Reckoning

Four years of multimodal odometry and mapping on the rail vehicles

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