Low cost IMU–Odometer–GPS ego localization for unusual maneuvers

Ndjeng, Alexandre Ndjeng; Gruyer, Dominique; Glaser, Sébastien; Lambert, Alain

doi:10.1016/j.inffus.2010.06.006

Cited by 43 publications

(8 citation statements)

References 14 publications

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“…They have concentrated on different subfields of research. In [5], least squares calibration is used; estimation techniques such as back-propagation and neural networks in [6]; GPS data fusion with internal odometry data in [7,8]. …”

Section: Introductionmentioning

confidence: 99%

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Valiente

Gil

Reinoso

et al. 2017

Sensors

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Abstract:This work presents an improved visual odometry using omnidirectional images. The main purpose is to generate a reliable prior input which enhances the SLAM (Simultaneous Localization and Mapping) estimation tasks within the framework of navigation in mobile robotics, in detriment of the internal odometry data. Generally, standard SLAM approaches extensively use data such as the main prior input to localize the robot. They also tend to consider sensory data acquired with GPSs, lasers or digital cameras, as the more commonly acknowledged to re-estimate the solution. Nonetheless, the modeling of the main prior is crucial, and sometimes especially challenging when it comes to non-systematic terms, such as those associated with the internal odometer, which ultimately turn to be considerably injurious and compromise the convergence of the system. This omnidirectional odometry relies on an adaptive feature point matching through the propagation of the current uncertainty of the system. Ultimately, it is fused as the main prior input in an EKF (Extended Kalman Filter) view-based SLAM system, together with the adaption of the epipolar constraint to the omnidirectional geometry. Several improvements have been added to the initial visual odometry proposal so as to produce better performance. We present real data experiments to test the validity of the proposal and to demonstrate its benefits, in contrast to the internal odometry. Furthermore, SLAM results are included to assess its robustness and accuracy when using the proposed prior omnidirectional odometry.

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Section: Introductionmentioning

confidence: 99%

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Valiente

Gil

Reinoso

et al. 2017

Sensors

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“…Depending on the application, this may include the car's dynamic state, road geometry [20,21], the driver's biomedical condition, the degree of the driver's distraction [22], discrete driving modes (like ''accelerating,'' ''standstill,'' ''going backwards,'' etc.) [23], manoeuvering intentions [24], and many more.…”

Section: Bayesian Trackingmentioning

confidence: 99%

“…In some applications the state either includes inherently discrete elements, e.g., if one assumes that a car has a finite number of driving modes [23], or only a priori chosen discrete values of a continuous variable shall be considered, as for the height of a pedestrian in [31]. In practice, interacting multiple models are employed in these cases.…”

Section: Detection and Trackingmentioning

confidence: 99%

Information fusion for automotive applications – An overview

2011

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“…Alternatively, a structural adaptation approach called Interacting Multiple Model (IMM) makes it possible for a set of models with different characteristics to run in parallel [21,22]. IMM algorithms have already been applied to vehicle localization, and are usually used to represent the possible vehicle driving patterns with a set of models, which are generally established according to different maneuvering or driving conditions [21,22,23].…”

Section: Introductionmentioning

confidence: 99%

“…IMM algorithms have already been applied to vehicle localization, and are usually used to represent the possible vehicle driving patterns with a set of models, which are generally established according to different maneuvering or driving conditions [21,22,23]. The IMM algorithm has shown better results than conventional switching schemes, because a smooth transition from one model to another is achieved [24].…”

Section: Introductionmentioning

confidence: 99%

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

Liu

Chan

2017

Sensors

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In this paper, we propose a cost-effective localization solution for land vehicles, which can simultaneously adapt to the uncertain noise of inertial sensors and bridge Global Positioning System (GPS) outages. First, three Unscented Kalman filters (UKFs) with different noise covariances are introduced into the framework of Interacting Multiple Model (IMM) algorithm to form the proposed IMM-based UKF, termed as IMM-UKF. The IMM algorithm can provide a soft switching among the three UKFs and therefore adapt to different noise characteristics. Further, two IMM-UKFs are executed in parallel when GPS is available. One fuses the information of low-cost GPS, in-vehicle sensors, and micro electromechanical system (MEMS)-based reduced inertial sensor systems (RISS), while the other fuses only in-vehicle sensors and MEMS-RISS. The differences between the state vectors of the two IMM-UKFs are considered as training data of a Grey Neural Network (GNN) module, which is known for its high prediction accuracy with a limited amount of samples. The GNN module can predict and compensate position errors when GPS signals are blocked. To verify the feasibility and effectiveness of the proposed solution, road-test experiments with various driving scenarios were performed. The experimental results indicate that the proposed solution outperforms all the compared methods.

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Low cost IMU–Odometer–GPS ego localization for unusual maneuvers

Cited by 43 publications

References 14 publications

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Information fusion for automotive applications – An overview

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

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