Odometry Calculation Research Articles

A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.

TRAIN HEADWAY OPTIMISATION USING VIRTUAL BLOCKS

Although the implementation of a new Automatic Train Protection (ATP) system increases the safety level, it also exerts some impact on the blocking time in block sections, and consequently, on the headway between trains. At the same time, ATP systems introduce a train positioning system based on odometry calculation and reporting back to the trackside system. This paper describes the concept of using virtual blocks based on train position reporting in the ATP system for purposes of non-occupancy determination. Virtual blocks can be used to reduce headways on railway lines without increasing the number of trackside signalling devices. Preliminary capacity assessment was performed to calculate the average headway depending on the signalling system with reference to a case study.

EFFECT OF TRAIN POSITION REPORTING ON RAILWAY LINE CAPACITY

Over the recent years, an increase in the demand for passenger and freight rail transport services has been observed. This is mainly owing to the policy objectives related to the use eco-friendly means of transport and the investment causing the quality of rail services to grow. Along with the growing demand for rail transport, a problem of railway capacity utilisation has emerged. Although the implementation of the new Automatic Train Protection (ATP) system increases the safety level, but under some scenarios, it may decrease the headways between trains. At the same time, the ATP systems enables train positioning based on odometry calculation and reporting it to the trackside system. This paper proposes a solution that involves application of virtual block sections which use the train position reporting feature of the ATP system for the determination of non-occupancy. Virtual blocks can successfully reduce headways in railway lines without increasing the number of trackside signalling devices. The capacity assessment proposed under the study was performed to calculate the average headway depending on the signalling system configuration.

SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM

Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.

Omni-Directional Mobile Robot Control using Raspberry Pi and Jetson Nano

The use of robots continues to increase in various fields in society today. Current developments require robots that are more effective and efficient in its application, especially in terms of its movement. This research is intended to design robots that move omni-directionally, making it easier to move in all directions by using the omniwheels, and the robots can detect simple object around them. The robot using 3 DC motors with encoder as feedback. System movement is controlled using Raspberry Pi 4, to move the robot to destination postition from user’s input. For robot to be able to detect certain object, the robot is equipped with infrared sensor for measure the distance and a camera for image processing purpose with jetson nano as a controller. By using inverse kinematics and odometry calculations for robot movement, it has an error of 9.51% on the x-axis and 8.12% on the y-axis at the robot's final position. The robot can detect objects using infrared sensors with error rate 0.87% and measure object sizes using a camera and image processing with error rate of 30.02% for object’s width readings and 41.8% for object’s height readings.

Toward the Ghosting Phenomenon in a Stereo-Based Map With a Collaborative RGB-D Repair

Although 3-D reconstruction of dynamic road environment by moving cameras has been broadly applied in recognition and navigation systems, this task is still considered challenging, especially under circumstances with moving objects, where the reconstruction precision is strongly harassed by the ghosting problem. To address this issue, in this paper, we propose a novel approach for reconstructing 3-D maps of complete static scenes, based on a combination of an elaborately designed moving-object filtering mechanism and a map repairing and blank refilling procedure, where both plausible color and depth information from stereo image pairs are utilized. In this approach, first, we employ the planarity knowledge into the initial depth map based on the simple linear iterative cluster (SLIC) superpixel segmentation. The dynamic area in the image is determined under the supervision of odometry calculation. After wiping off moving objects, by collaboratively repairing color and depth information, the final 3-D map containing only static scene is obtained. The experimental results on extensive challenging real-world scenarios demonstrate the effectiveness and robustness of our approach.

A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors.

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.

全方向移動マテリアルハンドリングロボットの試作

This paper describes prototyping of a semi-automated material handling robot that combines flexibility of operation by an operator and convenience of automation technology, to automate process of positioning when catching a raw fabric roll as a target in material handling cart. This robot has omnidirectional drive mechanism, kinematics based control system, and machine vision system for detect and approach the target. Its omnidirectional motion can be estimated through odometry calculation by analyzing forward kinematics. Machine vision system is also available to detect target position and posture using AR marker. At this stage, the robot can be controlled through a basic operation to approach a raw fabric roll by using above machine vision system.

Kinematic Analysis and Motion Planning for a Planar Multiarticulated Omnidirectional Mobile Robot

In this paper, kinematic modeling and singularity analysis are performed for an omnidirectional mobile robot. At the beginning, generalized formulas of the mobility and the first-order kinematics for a planar multiarticulated omnidirectional mobile robot are proposed. To verify the proposed formulas, a mobile robot that consists of three wheel mechanisms each of which has one redundant joint as compared to the operational degrees is introduced. Initially, the kinematic modeling and dynamic analysis, and odometry calculation for this robot are conducted. Next, singularity analysis of the proposed robot is performed. Then, using such a kinematic redundancy of each chain, motion planning algorithms that exploit the kinematic redundancy of the wheel chain are suggested. For navigation, a localization algorithm of the mobile robot based on odometry is presented, and specifically, two-leveled obstacle avoidance scheme, which simultaneously considers both large and small obstacles, is proposed. The usefulness of the proposed algorithms is verified through experimentation.

Laser interferometry measurements based calibration and error propagation identification for pose estimation in mobile robots

SUMMARYA widely used method for pose estimation in mobile robots is odometry. Odometry allows the robot in real time to reconstruct its position and orientation from the wheels' encoder measurements. Given to its unbounded nature, odometry calculation accumulates errors with quadratic increase of error variance with traversed distance. This paper develops a novel method for odometry calibration and error propagation identification for mobile robots. The proposed method uses a laser-based interferometer to measure distance precisely. Two variants of the proposed calibration method are examined: the two-parameter model and the three-parameter model. Experimental results obtained using a Khepera 3 mobile robot showed that both methods significantly increase accuracy of the pose estimation, validating the effectiveness of the proposed calibration method.