Fast Keypoint Features From Laser Scanner for Robot Localization and Mapping

Kallasi, Fabjan; Rizzini, Dario Lodi; Caselli, Stefano

doi:10.1109/lra.2016.2517210

Cited by 47 publications

(58 citation statements)

References 19 publications

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“…A novel approach is proposed by Kallasi et al who introduced a new method for detecting features to localize and navigate robots [19]. They propose two new feature detectors called Fast Adaptive Laser Keypoint Orientation-Invariant (FALKO) and Orthogonal Corner (OC).…”

Section: Recent Approaches In Robot Localization and Its Accuracymentioning

confidence: 99%

Creating a robot localization monitor using particle filter and machine learning approaches

Eder

Reip²,

Steinbauer

2021

Appl Intell

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Robot localization is a fundamental capability of all mobile robots. Because of uncertainties in acting and sensing, and environmental factors such as people flocking around robots, there is always the risk that a robot loses its localization. Very often behaviors of robots rely on a reliable position estimation. Thus, for dependability of robot systems it is of great interest for the system to know the state of its localization component. In this paper we present an approach that allows a robot to asses if the localization is still correct. The approach assumes that the underlying localization approach is based on a particle filter. We use deep learning to identify temporal patterns in the particles in the case of losing/lost localization. These patterns are then combined with weak classifiers from the particle set and sensor perception for boosted learning of a localization estimator. Through the extraction of features generated by neural networks and its usage for training strong classifiers, the robots localization accuracy can be estimated. The approach is evaluated in a simulated transport robot environment where a degraded localization is provoked by disturbances cased by dynamic obstacles. Results show that it is possible to monitor the robots localization accuracy using convolutional as well as recurrent neural networks. The additional boosting using Adaboost also yields an increase in training accuracy. Thus, this paper directly contributes to the verification of localization performance.

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Section: Recent Approaches In Robot Localization and Its Accuracymentioning

confidence: 99%

Creating a robot localization monitor using particle filter and machine learning approaches

Eder

Reip²,

Steinbauer

2021

Appl Intell

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“…In this paper, LiDAR intensity refers to the received power of the returned pulse from the targets. Some corresponding models have been published (Kallasi et al 2016;El-Sheimy and Youssef 2020;Guivant et al 2000;Yoshitaka et al 2006;Wolcott and Eustice 2015;Barfoot et al 2016;Hewitt and Marshall 2015). According to these previous investigations, LiDAR intensity is always influenced by certain intrinsic and extrinsic parameters.…”

Section: Intensity Modelmentioning

confidence: 99%

“…LiDAR sensors are active devices that obtain range information; they have been extensively employed in SLAM applications (Qian et al 2017;Chen et al 2018a, b, c;Tang et al 2015). LiDAR sensors have also become popular in autonomous driving applications that utilize SLAMbased technology to offer a robust positioning solution when Global Navigation Satellite System (GNSS) navigation signals are degraded or not available (Kallasi et al 2016;El-Sheimy and Youssef 2020). Range information is obtained by measuring the time of flight between the emitted pulse and the reflected laser echoes from targets.…”

Section: Introductionmentioning

confidence: 99%

A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM

et al. 2020

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Light Detection and Ranging (LiDAR) sensors are popular in Simultaneous Localization and Mapping (SLAM) owing to their capability of obtaining ranging information actively. Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy. However, before employing LiDAR intensities in SLAM, a calibration operation is usually carried out so that the intensity is independent of the incident angle and range. The range is determined from the laser beam transmitting time. Therefore, the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface. In a complex environment, it is difficult to obtain the incident angle robustly. This procedure also complicates the data processing in SLAM and as a result, further application of the LiDAR intensity in SLAM is hampered. Motivated by this problem, in the present study, we propose a Hyperspectral LiDAR (HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM. HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements. Owing to the design of the laser, the eight-channel range and intensity were collected with the same incident angle and range. According to the laser beam radiation model, the ratio values between two randomly selected channels’ intensities at an identical target are independent of the range information and incident angle. To test the proposed method, the HSL was employed to scan a wall with different coloured papers pasted on it (white, red, yellow, pink, and green) at four distinct positions along a corridor (with an interval of 60 cm in between two consecutive positions). Then, a ratio value vector was constructed for each scan. The ratio value vectors between consecutive laser scans were employed to match the point cloud. A classic Iterative Closest Point (ICP) algorithm was employed to estimate the HSL motion using the range information from the matched point clouds. According to the test results, we found that pink and green papers were distinctive at 650, 690, and 720 nm. A ratio value vector was constructed using 650-nm spectral information against the reference channel. Furthermore, compared with the classic ICP using range information only, the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation. For the best case in the field test, the proposed method enhanced the heading angle estimation by 72%, and showed an average 25.5% improvement in a featureless spatial testing environment. The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

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“…Keypoint detection is a basic but important step in many applications [33], such as registration [5], recognition [10], retrieval [34] and simultaneous location and mapping (SLAM) [35]. An appropriate keypoint detector has a reasonable effect on the descriptors' performance [36] (it will be discussed in the Section.VII).…”

Section: ) Keypoint Detectionmentioning

confidence: 99%

3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

Zhou

Jiang

2019

IEEE Access

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3D surface matching by local feature descriptors is a fundamental task in 3D object registration, recognition, and retrieval. In view of 2D projected descriptors' information compression and 3D directly voxelized descriptors' sensitivity to density variation and boundary, this paper first proposes a voxel-based buffer-weighted binary descriptor, named VBBD. After local surfaces of detected keypoints are voxelized, each voxel's buffer region is established, and the buffer-weighted Gaussian kernel density is calculated. If the current voxel's buffer-weighted density is larger than its local surface's average density, the voxel is labeled 1, otherwise, it is 0. The proposed descriptor has several merits: 1) direct acquisition of 3D information without projection, the neighbor information is less compressed. 2) voxels are labeled and binarized according to the buffer-weighted density, which improves the robustness to boundary effect, noise and density variation. Based on the VBBD, a global optimal surface matching method based on a Kuhn_Munkres (KM) algorithm is adopted. The strategy has several advantages: 1) by calculating the descriptors of large-scaled surfaces which are down sampled, the number of the point cloud is reduced, and meanwhile, large-scaled information is obtained; and 2) KM algorithm is adopted to find matching pairs to achieve final maximum weight sum of all matching pairs, which can efficiently avoid local optimum. The experimental results show that, compared with other state-of-the-art descriptors, the VBBD has better descriptiveness and robustness, and the surface matching strategy by the VBBD can achieve both high recall and precision.INDEX TERMS Voxel, Gaussian kernel density, binary descriptor, Kuhn_Munkres matching.

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Fast Keypoint Features From Laser Scanner for Robot Localization and Mapping

Cited by 47 publications

References 19 publications

Creating a robot localization monitor using particle filter and machine learning approaches

Creating a robot localization monitor using particle filter and machine learning approaches

A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM

3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

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