scholarly journals Environment map generation in forest using field robot

10.29007/x149 ◽  
2020 ◽  
Author(s):  
Noboru Takegami ◽  
Eiji Hayashi
Keyword(s):  
Forest Area ◽  
Employment Rate ◽  
Robot Operating System ◽  
Environment Map ◽  
Localization And Mapping ◽  
Map Generation ◽  
Unmanaged Forest ◽  
Forest Operation ◽  
Environmental Map ◽  
Field Robot

We are developing an autonomous field robot to save labor in forest operation. About half of Japan's artificial forest area is already available as wood. However, trees are not harvested and forest resources are not effectively used, because the labor and costs are not sufficient. The employment rate of young people in forestry tends to decline, and the unmanaged forest area is expected to increase in the future. Therefore, in our laboratory we propose an autonomous field robot with all terrain vehicles (ATV) that focuses on the automation of work. The robot we are developing automates weeding and observation for all trees in the forest. In this research, we introduced Robot Operating System (ROS) developed in recent years to this robot. In addition, we observed trees by generating an environmental map in the forest using Simultaneous Localization and Mapping (SLAM).

scholarly journals Environment map generation in forest using field robot

2020 ◽  
Vol 25 ◽  
pp. 179-182
Author(s):  
Noboru Takegami ◽  
Eiji Hayashi ◽  
Ryusuke Fujisawa
Keyword(s):  
Environment Map ◽  
Map Generation ◽  
Field Robot

scholarly journals Profiling NVIDIA Jetson Embedded GPU Devices for Autonomous Machines

2020 ◽  
Author(s):  
Yazhou Li ◽  
Yahong Rosa Zheng
Keyword(s):  
Effective Means ◽  
Field Testing ◽  
Operating Mode ◽  
Autonomous Driving ◽  
Robot Operating System ◽  
Race Car ◽  
Localization And Mapping ◽  
Autonomous Machines ◽  
Particle Filter Algorithm ◽  
Embedded Gpu

This paper presents two methods, tegrastats GUI version jtop and Nsight Systems, to profile NVIDIA Jetson embedded GPU devices on a model race car which is a great platform for prototyping and field testing autonomous driving algorithms. The two profilers analyze the power consumption, CPU/GPU utilization, and the run time of CUDA C threads of Jetson TX2 in five different working modes. The performance differences among the five modes are demonstrated using three example programs: vector add in C and CUDA C, a simple ROS (Robot Operating System) package of the wall follow algorithm in Python, and a complex ROS package of the particle filter algorithm for SLAM (Simultaneous Localization and Mapping). The results show that the tools are effective means for selecting operating mode of the embedded GPU devices.

A New Approach to Sonar Based Indoor Mapping Localization

2005 ◽  
Author(s):  
A. Meghdari ◽  
K. Kobravi ◽  
H. Safyallah ◽  
M. Moeeni ◽  
Y. Khatami ◽  
...  
Keyword(s):  
Efficient Algorithm ◽  
Robot Navigation ◽  
Laser Scanner ◽  
Indoor Environments ◽  
Scanner Data ◽  
Narrow Beam ◽  
New Approach ◽  
Vehicle Localization ◽  
Environment Map ◽  
Map Generation

Vehicle localization and environment mapping are the most essential parts of the robot navigation in unknown environments. Since the problem of localization in indoor environments is directly related to the problem of online map generation, in this paper a new and efficient algorithm for simultaneous localization and map generation is proposed and novel results for real environments are achieved. This new algorithm interprets and validates the raw sonar measurements in first step, and applies them to the environment map in the next step. There are various adjustable parameters which make the algorithm flexible for different sonar types. This algorithm is efficient and is robust to sonar failure; if sonar does not work properly data can be discarded. These abilities make the algorithm efficient for sonar navigation in flat environments even by poor sonar and odometers perception data. This algorithm has the ability of matching with various types of sonar and even to be used with laser scanner data, whenever each laser scanner data is treated as multiple sonar detections with narrow beam detection patterns.

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

scholarly journals An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1742 ◽  
Author(s):  
Chuang Qian ◽  
Hongjuan Zhang ◽  
Jian Tang ◽  
Bijun Li ◽  
Hui Liu
Keyword(s):  
Laser Scanner ◽  
Location Based Services ◽  
Experimental Result ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization And Mapping ◽  
Map Generation ◽  
Representation Method ◽  
Rigid Body Transformation ◽  
Grid Based

An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.

Teaching-Playback Navigation Without a Consistent Map

2018 ◽  
Vol 30 (4) ◽  
pp. 591-597 ◽  
Author(s):  
Naoki Akai ◽  
Luis Yoichi Morales ◽  
Hiroshi Murase ◽  
◽  
Keyword(s):  
Autonomous Navigation ◽  
Large Scale ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Environment Map ◽  
Localization And Mapping ◽  
Mapping Process ◽  
Line Processes ◽  
Target Path ◽  
Navigation Method

This paper presents a teaching-playback navigation method that does not require a consistent map built using simultaneous localization and mapping (SLAM). Many open source projects related to autonomous navigation including SLAM have been made available recently; however, autonomous mobile robot navigation in large-scale environments is still difficult because it is difficult to build a consistent map. The navigation method presented in this paper uses several partial maps to represent an environment map. In other words, the complex mapping process is not necessary to begin autonomous navigation. In addition, the trajectory that the robot travels in the mapping phase can be directly used as a target path. As a result, teaching-playback autonomous navigation can be achieved without any off-line processes. We tested the navigation method using log data taken in the environment of the Tsukuba Challenge and the testing results show its performance. We provide source code for the navigation method, which includes modules required for autonomous navigation (https://github.com/NaokiAkai/AutoNavi).

809 A weighted environment map generation using vision sensors for an autonomous flight system of UAV

2013 ◽  
Vol 2013.51 (0) ◽  
pp. _809-1_-_809-2_
Author(s):  
Satoki SAWA ◽  
Yohei MIYAUCHI ◽  
Maroi KODAMA ◽  
Masashi MIURA ◽  
Kazunori SAKURAMA ◽  
...  
Keyword(s):  
Flight System ◽  
Autonomous Flight ◽  
Environment Map ◽  
Vision Sensors ◽  
Map Generation
Sign in / Sign up

Export Citation Format

Share Document