VINS on wheels

Wu, Kan; Guo, Chao; Γεωργίου, Γεώργιος; Roumeliotis, Stergios I.

doi:10.1109/icra.2017.7989603

Cited by 154 publications

(91 citation statements)

References 16 publications

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“…Assumptions that v lat n and v up n are roughly null are common for cars moving forward on human made roads or wheeled robots moving indoor. Treating them as loose constraints, i.e., allowing the uncertainty encoded in N n to be non strictly null, leads to much better estimates than treating them as strictly null [13].…”

Section: B Defining the Pseudo-measurements H(·)mentioning

confidence: 99%

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AI-IMU Dead-Reckoning

Brossard

Barrau

Bonnabel

2020

IEEE Trans. Intell. Veh.

182

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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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Section: B Defining the Pseudo-measurements H(·)mentioning

confidence: 99%

“…As concerns wheeled vehicles, taking into account vehicle constraints and odometer measurements are known to increase the robustness of localization systems [13]- [16]. Although quite successful, such systems continuously process a large amount of data which is computationally demanding and energy consuming.…”

Section: Introductionmentioning

confidence: 99%

AI-IMU Dead-Reckoning

Brossard

Barrau

Bonnabel

2020

IEEE Trans. Intell. Veh.

182

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“…The deep learning based detector (see Section IV-A) has of course not been trained or cross-validated on this sequence. inertial systems [17,18]. Although quite successful, systems using vision continuously process a large amount of data which is computationally demanding and energy consuming.…”

Section: A Related Workmentioning

confidence: 99%

“…For each epoch, we organize data as a batch of 2 min sequences, where we randomly set the start instant of each sequence, and constraints each starting sequence to be a stop of at minimum 1 s. Training the full detector takes less than one day with a GTX 1080 GPU. 15,16,17 in terms of: m-ATE /aligned m-ATE / final distance error to ground truth, in m. Last line is the concatenation of the three sequences. Direct IMU integration always diverges.…”

Section: B Detector Trainingmentioning

confidence: 99%

RINS-W: Robust Inertial Navigation System on Wheels

Brossard

Barrau

Bonnabel

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper proposes a real-time approach for longterm inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final precision of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-ofthe-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.

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“…Most INS rely on a 6-axis inertial measurement unit (IMU) that measures the local linear acceleration and angular velocity of the platform to which it is rigidly connected. With the recent advancements of hardware design and manufacturing, low-cost light-weight micro-electro-mechanical (MEMS) IMUs have become ubiquitous [3,4,5], which enables high-accuracy localization for, among others, mobile devices [6] and micro aerial vehicles (MAVs) [7,8,9,10,11], holding huge implications in a wide range of emerging applications from mobile augmented reality (AR) [12,13] and virtual reality (VR) [14] to autonomous driving [15,16]. Unfortunately, simple integration of high-rate IMU measurements that are corrupted by noise and bias, often results in pose estimates unreliable for long-term navigation.…”

Section: Introductionmentioning

confidence: 99%

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

281

119

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As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work -which is unfortunately missing in the literature while being greatly demanded by researchers and engineers -in the hope to accelerate the VINS research and beyond in our society as a whole.where δq describes the small rotation that causes the true and estimated attitude to coincide. The advantage of this parametrization permits a minimal representation, 3 × 3 covariance matrix E I θ I θ T , for the attitude uncertainty.

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VINS on wheels

Cited by 154 publications

References 16 publications

AI-IMU Dead-Reckoning

AI-IMU Dead-Reckoning

RINS-W: Robust Inertial Navigation System on Wheels

Visual-Inertial Navigation: A Concise Review

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