Pose and covariance matrix propagation issues in cooperative localization with LiDAR perception

Héry, Elwan; Xu, Philippe

doi:10.1109/ivs.2019.8813880

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…It can be seen from these results that the proposed pseudo‐inverse minimization and point‐to‐line matching gives the best consistency, even though the metric distance may be less accurate. Further experimental results with other matching metrics can be found in Héry et al (2018).…”

Section: Resultsmentioning

confidence: 99%

“…To use the shortest distances during the minimization, point‐to‐line matching is used. We have previously (Héry et al, 2018) compared this type of matching to other matchings. Once the closest segment

j

is found, we link the LiDAR point

p_{i}

to the line defined by the point

m_{j}

and the normal

n_{j}

to this segment at this point.…”

Section: Consistent Relative Localizationmentioning

confidence: 99%

“…We have simplified this algorithm to obtain a 2D point‐to‐line version. We have previously (Héry et al, 2018) compared this minimization to an alternative minimization. The solution chosen to avoid the nonlinearity of the transformation matrix

normalΔ T

is to use a first‐order small‐angle approximation:

normalΔ T = (][, \begin{matrix} \cos (normalΔ θ) & - \sin (normalΔ θ) & normalΔ x \\ \sin (normalΔ θ) & \cos (normalΔ θ) & normalΔ y \\ 0 & 0 & 1 \end{matrix}) \approx (][, \begin{matrix} 1 & - normalΔ θ & normalΔ x \\ normalΔ θ & 1 & normalΔ y \\ 0 & 0 & 1 \end{matrix}) .

…”

Section: Consistent Relative Localizationmentioning

confidence: 99%

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Consistent decentralized cooperative localization for autonomous vehicles using LiDAR, GNSS, and HD maps

Héry

2021

Journal of Field Robotics

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To navigate autonomously, a vehicle must be able to localize itself with respect to its driving environment and the vehicles with which it interacts. This work presents a decentralized cooperative localization method. It is based on the exchange of Local Dynamic Maps (LDM), which are cyberphysical representations of the physical driving environment containing poses and kinematic information about nearby vehicles. An LDM acts as an abstraction layer that makes the cooperation framework sensor-agnostic, and it can even improve the localization of a sensorless communicating vehicle. With this goal in mind, this work focuses on the property of consistency in LDM estimates. Uncertainty in the estimates needs to be properly modeled, so that the estimation error can be statistically bounded for a given confidence level. To obtain a consistent system, we first introduce a decentralized fusion framework that can cope with LDMs whose errors have an unknown degree of correlation. Second, we present a consistent method for estimating the relative pose between vehicles, using a 2D LiDAR with a point-to-line metric within an iterative-closest-point approach, combined with communicated polygonal shape models. Finally, we add a bias estimator in order to reduce position errors when non-differential GNSS receivers are used, based on visual observations of features geo-referenced in a High-Definition (HD) map. Real experiments were conducted, and the consistency of our approach was demonstrated on a platooning scenario using two experimental vehicles. The full experimental dataset used in this work is publicly available. A Poses transformations operatorsAs we use only direct orthonormal frames, the definition of one frame in its reference frame is equivalent to the frame transformation between these two frames and to the pose of this frame in its reference frame. The position corresponds to the origin and the orientation to the one of the x axes of the frame.

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Section: Resultsmentioning

confidence: 99%

j

is found, we link the LiDAR point

p_{i}

to the line defined by the point

m_{j}

and the normal

n_{j}

to this segment at this point.…”

Section: Consistent Relative Localizationmentioning

confidence: 99%

normalΔ T

is to use a first‐order small‐angle approximation:

normalΔ T = (][, \begin{matrix} \cos (normalΔ θ) & - \sin (normalΔ θ) & normalΔ x \\ \sin (normalΔ θ) & \cos (normalΔ θ) & normalΔ y \\ 0 & 0 & 1 \end{matrix}) \approx (][, \begin{matrix} 1 & - normalΔ θ & normalΔ x \\ normalΔ θ & 1 & normalΔ y \\ 0 & 0 & 1 \end{matrix}) .

…”

Section: Consistent Relative Localizationmentioning

confidence: 99%

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Consistent decentralized cooperative localization for autonomous vehicles using LiDAR, GNSS, and HD maps

Héry

2021

Journal of Field Robotics

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“…Indeed, the multiplication of the estimations makes it possible to eliminate the outliers as highlighted in [67]. Moreover, cooperative systems allow the extension of covered areas and fields of view, which again increases the reliability and precision of the estimations [42], [68], [69]. The other interest of cooperation in a localization context lies in the reduction of costs.…”

Section: Localizationmentioning

confidence: 99%

Survey on Cooperative Perception in an Automotive Context

Caillot

Ouerghi

Vasseur

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The idea of cooperation has been introduced to selfdriving cars about a decade ago with the aim to reduce the occlusion caused by other users or the scene. More recently, the research efforts turned toward cooperative infrastructure bringing a new kind of the point of view as well as more processing power. This paper lies in this new field providing a survey that addresses the cooperative environment. We provide an overview of the architectures available to create such a system as well as the challenges introduced by the cooperation. Later, we review the main blocks involved in the perception: localization, object detection & tracking, map generation. Each block is reviewed under the prism of cooperation. We also provide a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of the cooperative perception as well as a list of related scenarios alongside experimentations. Finally, we list some related datasets before concluding our paper, underlining the perspectives for further works.

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“…SLAMbased approaches [6]. Alternatively the enhancement can be performed via multiple vehicles cooperation [5], [12].…”

Section: North Eastmentioning

confidence: 99%

High Integrity Lane-level Occupancy Estimation of Road Obstacles Through LiDAR and HD Map Data Fusion

Bernardi

Masi

2020

2020 IEEE Intelligent Vehicles Symposium (IV)

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In the paper a fast and consistent method to manage uncertainties on detected traffic agents providing reliable results is presented. The information provided by a LiDARbased object detector is combined with a high-definition map to identify the drivable space of the carriageway. Because the use of a HD map requires the use of a localization system, the uncertainty of the estimated pose shall be handled carefully. A novel approach taking into account the localization uncertainty in the perception task by direct propagation of it onto the LiDAR points is proposed. It is compared with a classical propagation that relies on linearized approximation. The good performances of this approach in terms of integrity are demonstrated by the use of real data acquired at the entrance of a roundabout being a particularly complex situation.

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Pose and covariance matrix propagation issues in cooperative localization with LiDAR perception

Cited by 7 publications

References 19 publications

Consistent decentralized cooperative localization for autonomous vehicles using LiDAR, GNSS, and HD maps

Consistent decentralized cooperative localization for autonomous vehicles using LiDAR, GNSS, and HD maps

Survey on Cooperative Perception in an Automotive Context

High Integrity Lane-level Occupancy Estimation of Road Obstacles Through LiDAR and HD Map Data Fusion

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