Estimation of the Speed From the Odometer Readings Using Optimized Curve-Fitting Filter

Nemec, Dušan; Hruboš, Marián; Janota, Aleš; Pirník, Rastislav; Gregor, Michal

doi:10.1109/jsen.2020.3023503

Cited by 5 publications

(5 citation statements)

References 21 publications

(18 reference statements)

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“…In order to improve upon the localization and mapping results, two different strategies were adopted. The sharp or abrupt changes in the trajectory were improved by employing a curve fitting derivative filter (CFDT), similar to the one proposed in [ 39 ], to the successive localization positions. As the proposed method suffers from slight drift in terms of error in the case of larger distance and number of scans, the accuracy of the proposed method was sought to be improved by incorporating some constraints like loop closure.…”

Section: Resultsmentioning

confidence: 99%

Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications

Aijazi,

Checchin

2024

Sensors

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Three-dimensional point cloud registration is a fundamental task for localization and mapping in autonomous navigation applications. Over the years, registration algorithms have evolved; nevertheless, several challenges still remain. Recently, non-repetitive scanning LiDAR sensors have emerged as a promising 3D data acquisition tool. However, the feasibility of this type of sensor to leverage robust point cloud registration still needs to be ascertained. In this paper, we explore the feasibility of one such LiDAR sensor with a Spirograph-type non-repetitive scanning pattern for robust 3D point cloud registration. We first characterize the data of this unique sensor; then, utilizing these results, we propose a new 3D point cloud registration method that exploits the unique scanning pattern of the sensor to register successive 3D scans. The characteristic equations of the unique scanning pattern, determined during the characterization phase, are used to reconstruct a perfect scan at the target distance. The real scan is then compared with this reconstructed scan to extract objects in the scene. The displacement of these extracted objects with respect to the center of the unique scanning pattern is compared in successive scans to determine the transformations that are then used to register these scans. The proposed method is evaluated on two real and different datasets and compared with other state-of-the-art registration methods. After analysis, the performance (localization and mapping results) of the proposed method is further improved by adding constraints like loop closure and employing a Curve Fitting Derivative Filter (CFDT) to better estimate the trajectory. The results clearly demonstrate the suitability of the sensor for such applications. The proposed method is found to be comparable with other methods in terms of accuracy but surpasses them in performance in terms of processing time.

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Section: Resultsmentioning

confidence: 99%

Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications

Aijazi,

Checchin

2024

Sensors

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“…where δx t0 is the initial condition vector of the system and Φ is the transition matrix. A closed form solution of the transition matrix in (18) was offered in [33], [34]:…”

Section: Proposed Approachmentioning

confidence: 99%

“…The IMU belongs to this group together with odometry. In odometry, sensors are installed next to the wheels [17], [18]. Relying on the pre-determined wheel diameter and wheelbase, the position and heading are achieved.…”

Section: Introductionmentioning

confidence: 99%

MoRPI: Mobile Robot Pure Inertial Navigation

Etzion,

Klein

2023

J. Ind. Sea. Pos. Nav.

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Mobile robots are used in a variety of applications indoors and outdoors. In real-world scenarios, frequently, the navigation solution relies only on the inertial sensors. Consequently, the navigation solution drifts in time. In this paper, we propose the MoRPI framework, a mobile robot pure inertial approach. Instead of travelling in a straight line trajectory, the robot moves in a periodic motion trajectory to enable peak-to-peak estimation. Two types of MoRPI approaches are suggested, one is based on both accelerometer and gyroscope readings while the other requires only the gyroscopes. Closed form analytical solutions are derived to show that MoRPI produces lower position error compared to the classical pure inertial solution. In addition, field experiments were made with a mobile robot equipped with two different types of inertial sensors. The results show the benefits of using our approach. To facilitate further development, both the dataset and code are publicly available in https://github.com/ansfl/MoRPI.

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“…There are advanced in-vehicle systems that track vehicle movement and calculate speed based on measurements of various parameters using steering wheel rotation, wheel spin speed, odometry, etc. [6], but this is mainly related to in-vehicle systems.…”

Section: Localization Issue In Road Tunnelsmentioning

confidence: 99%

Proximity Approach to Bluetooth Low Energy-Based Localization in Tunnels

et al. 2023

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Smart cities and smart mobility necessitate the development of newly adapted approaches and applications that ensure data connection among their particular parts; thus, the complex systems of smart technologies can be applied. Especially in the field of transport, mobility, and smart solutions, many of these applications require precise localization. Global navigation satellite system (GNSS) localization is used mainly because of its availability, reliability, and sufficient precision. However, there are also blind spots in smart cities where the GNSS is not available, e.g., in tunnels. This article presents an approach to fixing the issue of localization in road tunnels based on telecommunications with Bluetooth low energy (BLE) based infrastructure equipment. This approach enables us to ensure sufficient data resources for localization, and thus, smart applications can also be applied in the tunnels. At the same time, this approach also allows the fulfillment of individual needs according to the specifications of each tunnel or smart application. The outputs from testing and measurements are presented and discussed.

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Estimation of the Speed From the Odometer Readings Using Optimized Curve-Fitting Filter

Cited by 5 publications

References 21 publications

Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications

Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications

MoRPI: Mobile Robot Pure Inertial Navigation

Proximity Approach to Bluetooth Low Energy-Based Localization in Tunnels

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