Multilayer Laser Scanner Research Articles

MOVING OBJECT CLASSIFICATION USING MULTILAYER LASER SCANNING WITH SPACE SUBDIVISION FRAMEWORK

Abstract. In this paper, we focus on the development of intelligent construction vehicles to improve the safety of workers in construction sites. Generally, global navigation satellite system positioning is utilized to obtain the position data of workers and construction vehicles. However, construction fields in urban areas have poor satellite positioning environments. Therefore, we have developed a 3D sensing unit mounted on a construction vehicle for worker position data acquisition. The unit mainly consists of a multilayer laser scanner. We propose a real-time object measurement, classification and tracking methodology with the multilayer laser scanner. We also propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers. We applied the space subdivision framework with a geofencing approach using real-time object classification and tracking results estimated from temporal point clouds. Our methodology was evaluated using temporal point clouds acquired from a construction vehicle in drilling works.

Cyclist detection and tracking based on multi-layer laser scanner

The technology of Artificial Intelligence (AI) brings tremendous possibilities for autonomous vehicle applications. One of the essential tasks of autonomous vehicle is environment perception using machine learning algorithms. Since the cyclists are the vulnerable road users, cyclist detection and tracking are important perception sub-tasks for autonomous vehicles to avoid vehicle-cyclist collision. In this paper, a robust method for cyclist detection and tracking is presented based on multi-layer laser scanner, i.e., IBEO LUX 4L, which obtains four-layer point cloud from local environment. First, the laser points are partitioned into individual clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method based on subarea. Then, 37-dimensional feature set is optimized by Relief algorithm and Principal Component Analysis (PCA) to produce two new feature sets. Support Vector Machine (SVM) and Decision Tree (DT) classifiers are further combined with three feature sets, respectively. Moreover, Multiple Hypothesis Tracking (MHT) algorithm and Kalman filter based on Current Statistical (CS) model are applied to track moving cyclists and estimate the motion state. The performance of the proposed cyclist detection and tracking method is validated in real road environment.

Initial Results of Testing a Multilayer Laser Scanner in a Collision Avoidance System for Light Rail Vehicles

This paper presents an application to detect and track obstacles using a multilayer laser scanner. The goal of the detection system is to develop collision avoidance for the Light Rail Vehicle (LRV). The laser scanner, which is mounted in front of the tram, collects information in a four-scan plane. The object recognition and tracking module, which is composed of a three sub-modules segmentation, classification, and Kalman Filter tracking, was carried out on the raw data. Thus, data were provided for collision avoidance module. The proposed system was applied to a tram named “Silkworm” which is manufactured by Durmazlar Machine Inc. (Bursa, Turkey) and initial experimental tests have been conducted at the facilities of Durmazlar Machine Inc. in the city of Bursa, Turkey. This study aims to illustrate parts of the possible tests that can be carried out and to share with the scientific community an important application of multilayer laser scanners, although in the initial implementation phase, in urban rail transportation.

People and Vehicles Recognition Using Multi-layer Laser Scanner Based on Machine Learning

People and Vehicles Recognition Using Multi-layer Laser Scanner Based on Machine Learning

レーザスキャナを搭載する複数移動ロボットによる非GNSS環境下での協調移動物体追跡

This paper presents a laser-based tracking (estimation of pose and size) of moving objects using multiple mobile robots in Global-navigation-satellite-system (GNSS)-denied environments. Each robot is equipped with a multilayer laser scanner and detects moving objects, such as people, cars, and bicycles, in its own laser-scanned images by applying an occupancy-grid-based method. It then sends measurement information related to the moving objects to a central server. The central server estimates the objects’ poses (positions and velocities) and sizes from the measurement information using Bayesian filter. In this cooperative-tracking method, the nearby robots always share their tracking information, allowing tracking of invisible or partially visible objects. To perform a reliable cooperative tracking, robots have to correctly identify their relative pose. To do so in GNSS-denied environments, the relative pose is estimated by scan matching using laser measurements captured by both sensor nodes. Such cooperative scan matching is performed by 4-points-congruent-sets (4PCS)-matching-based coarse registration and Iterative-closest-point (ICP)-based fine registration methods. The experimental results of tracking a car, a motorcycle, and a pedestrian with two robots in an outdoor GNSS-denied environment validate the proposed method.

Vehicles and People Recognition Using Highly-resolved Multilayer Laser Scanner based on Support Vector Machine

Vehicles and People Recognition Using Highly-resolved Multilayer Laser Scanner based on Support Vector Machine

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

Pedestrian/Vehicle Detection Using a 2.5-D Multi-Layer Laser Scanner

Laser scanners are widely used as the primary sensor for autonomous driving. When the commercialization of autonomous driving is considered, a 2.5-D multi-layer laser scanner is one of the best sensor options. In this paper, a new method is presented to detect pedestrians and vehicles using a 2.5-D multi-layer laser scanner. The proposed method consists of three steps: 1) segmentation; 2) feature extraction; and 3) classification; this paper focuses on the last two steps. In feature extraction, new features for the multi-layer laser scanner are proposed to improve the classification performance. In classification, radial basis function additive kernel support vector machine is employed to reduce the computation time while maintaining the performance. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in many real-life situations.

マルチレイヤレーザスキャナを搭載する複数移動センサノードによる協調移動物体追跡

This paper presents a cooperative moving-object tracking with multiple mobile sensor nodes (mobile robots) using in-vehicle multilayer laser scanner. Each sensor node detects moving objects such as people, cars, and motorcycles using its own multilayer laser scanner by an occupancy-grid-based method and then estimate their sizes and postures. After that, each sensor node sends the estimates to a central server. The central server combines the estimates received from mobile sensor nodes to improve the tracking accuracy, and their estimates are fed back to the mobile sensor nodes. Experimental results of tracking vehicles by two mobile sensor nodes show the performance of the proposed method.

NDT スキャンマッチングに基づく車載レーザスキャナ観測点群の高精度マッピング

This paper presents a method of accurately refining 3D laser scan data from in-vehicle multilayer laser scanner. The vehicle estimates its own 3D pose (position and attitude) based on Normal-Distributions-Transform (NDT) scan-matching method. Based on the pose information, the vehicle pose when the laser scan data is captured is extrapolated and interpolated under the assumption that the vehicle moves at an almost constant linear and turning velocities. The extrapolated and interpolated pose of the vehicle is applied to mapping laser scan data into the voxel map represented on the world coordinate frame. Subsequently, the vehicle again estimates its own 3D pose based on NDT scan matching. Such an iteration enable to compensate the distortion of laser scan date and to accurately map the data on the world coordinate frame. Experimental results show the performance of the proposed method.

Robust object segmentation using a multi-layer laser scanner.

The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations.

Collision detection system design using a multi-layer laser scanner for collision mitigation

Collision detection plays a key role in collision mitigation systems. The malfunction of a collision mitigation system can result in a dangerous or unexpected situation for the driver and passengers. In order to prevent this situation, reliable collision detection algorithms are essential in terms of the collision time, the overlap and the collision decision. This study focuses on the reliable determination of the time to collision and the frontal overlap between the ego vehicle and the objects. The path prediction method using information on the ego vehicle is proposed to improve the accuracies of the time to collision and the frontal overlap calculations. The proposed algorithm is developed on the basis of a multi-layer laser scanner. The procedure of collision detection includes three steps. The first step is to select a high-risk object among multiple objects, the second step is to judge the time to collision and the frontal overlap to the high-risk object and, finally, the third step is to decide whether a crash is imminent or not. The performance of the proposed detection algorithm is validated in simulations and experiments.

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about the distance distributed according to four planes. Like a vehicle, a pedestrian constitutes, in the vehicle environment, an obstacle that must be detected, and located and then identified and tracked if necessary. To improve the robustness of pedestrian detection using a single laser sensor, a detection system based on the fusion of information located in the four laser planes is proposed. The method uses a nonparametric kernel-density-based estimation of pedestrian position of each laser plane. The resulting pedestrian estimations are then sent to a decentralized fusion according to the four planes. Temporal filtering of each object is finally achieved within a stochastic recursive Bayesian framework (particle filter), allowing a closer observation of pedestrian random movement dynamics. Many experimental results are given and validate the relevance of our pedestrian-detection algorithm with regard to a method using only a single-row laser-range scanner.