Structure tensors for general purpose LIDAR feature extraction

Li, Yangming; Olson, Edwin

doi:10.1109/icra.2011.5979567

Cited by 37 publications

(18 citation statements)

References 24 publications

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“…Diewald et al [10] use the Parzen window and then Gaussian kernels to detect the bridges in the scene whereas we want to detect objects at variable and fast speeds (PTWs). Li et al [11] propose a tensor-based method for a general purpose feature detector but this method constructs a scene of the static environment which is not our case study. Savoye et al [12] suggest an algorithm for performing cage-based tracking over time to visualize 3D data and for virtual reality applications but this method needs multiple sensors which is not our case as we work with a single-plane lidar.…”

Section: Research Work Done To Datementioning

confidence: 99%

A lidar-based method for the detection and counting of Powered Two Wheelers

Prabhakar¹,

Subirats²,

Lecomte³

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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The safety of Powered Two Wheelers (PTWs) is important for public authorities and road administrators around the world. Official figures show that PTW represent only 2% of the total traffic on French roads, but as these figures are obtained by simply counting the number plates registered, they do not give a true picture of the PTWs on the road at any given moment. To date, there is no overall solution to this problem that uses a sensor capable of detecting PTWs and taking into consideration their interaction with the other vehicles on the road (for example: I nter-lane traffic, when PTWs move in between two lanes on a highway), and no stateof-the-art technical solutions can be adapted to measure this category of vehicle in traffic (unlike cars and trucks). The research work in this domain has therefore, not been greatly developed which is an issue of concern. I n this paper we present a new method of detecting PTWs by using a single-plane lidar, named the Last Line Check (LLC) method. This method uses the energy of the last scan to extract the information. After extraction, a Support Vector M achine (SVM ) is used for classification. The LLC method is tested in real time and gives interesting results with a high precision.

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Section: Research Work Done To Datementioning

confidence: 99%

A lidar-based method for the detection and counting of Powered Two Wheelers

Prabhakar¹,

Subirats²,

Lecomte³

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Algorithms developed during this time were typically designed to extract feature points from LiDAR point-cloud data which is typically a collection of 3D range measurements collected on a discretized grid imposed on the sphere. 10,11 For some sensors, e.g., the Faro/SICK LiDAR sensors, range information is complemented with a surface reflectance measurement. However, due to the low resolution and significant sensor noise inherent in these measurements, most algorithms discard this information and work directly on the point cloud data.…”

Section: Prior Workmentioning

confidence: 99%

“…The complete descriptor is formed by concatenating results of a sequence of intensity and depth tests into a single binary value as shown in equation (11).…”

Section: An Igrand Feature Descriptor Based On Orbmentioning

confidence: 99%

iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications

Willis

Brink

2016

SPIE Proceedings

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This article describes a new 3D RGBD image feature, referred to as iGRaND, for use in real-time systems that use these sensors for tracking, motion capture, or robotic vision applications. iGRaND features use a novel local reference frame derived from the image gradient and depth normal (hence iGRaND) that is invariant to scale and viewpoint for Lambertian surfaces. Using this reference frame, Euclidean invariant feature components are computed at keypoints which fuse local geometric shape information with surface appearance information. The performance of the feature for real-time odometry is analyzed and its computational complexity and accuracy is compared with leading alternative 3D features.

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“…A Gaussian curve was fit to the sensor measurement in order to estimate the variance of the sensor noise. A smoothing filter adopted from [21] [22] was applied to sensor measurements. The averaged errors, standard deviations, 99th percentile, and maximum values are compared in Table I.…”

Section: A Zero Grip Force Estimationmentioning

confidence: 99%

Dynamic modeling of cable driven elongated surgical instruments for sensorless grip force estimation

Miyasaka

Haghighipanah

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Haptic feedback plays a key role in surgeries, but it is still a missing component in robotic Minimally Invasive Surgeries. This paper proposes a dynamic modelbased sensorless grip force estimation method to address the haptic perception problem for commonly used elongated cabledriven surgical instruments. Cable and cable-pulley properties are studied for dynamic modeling; grip forces, along with driven motor and gripper jaw positions and velocities are jointly estimated with Unscented Kalman Filter and only motor encoder readings and motor output torques are assumed to be known. A bounding filter is used to compensate for model inaccuracy and to improve method robustness. The proposed method was validated on a 10mm gripper which is driven by a Raven-II surgical robot. The gripper was equipped with 1dimensional force sensors which served as ground truth data. The experimental results showed that the proposed method provides sufficiently good grip force estimation, while only motor encoder and the motor torques are used as observations.

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Structure tensors for general purpose LIDAR feature extraction

Cited by 37 publications

References 24 publications

A lidar-based method for the detection and counting of Powered Two Wheelers

A lidar-based method for the detection and counting of Powered Two Wheelers

iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications

Dynamic modeling of cable driven elongated surgical instruments for sensorless grip force estimation

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