The Understanding of ON-Edge Motion Detection Through the Simulation Based on the Connectome of ’s Optic Lobe

The optic lobe of the fly is one of the prominent model systems for the neural mechanism of the motion detection. How a fly who lives under various visual situations of the nature processes the information from at most a few thousands of ommatidia in their neural circuit for the detection of moving objects is not exactly clear though many computational models of the fly optic lobe as a moving objects detector were suggested. Here we attempted to elucidate the mechanisms of ON-edge motion detection by a simulation approach based on the TEM connectome of Drosophila. Our simulation model of the optic lobe with the NEURON simulator that covers the full scale of ommatidia, reproduced the characteristics of the receptor neurons, lamina monopolar neurons, and T4 cells in the lobula. The contribution of each neuron can be estimated by changing synaptic connection strengths in the simulation and measuring the response to the motion stimulus. Those show the paradelle pathway provide motion detection in the fly optic lobe has more robustness and is more sophisticated than a simple combination of HR and BL systems.

Visual Navigation Based on Semantic Segmentation Using Only a Monocular Camera as an External Sensor

The most popular external sensor for robots capable of autonomous movement is 3D LiDAR. However, cameras are typically installed on robots that operate in environments where humans live their daily lives to obtain the same information that is presented to humans, even though autonomous movement itself can be performed using only 3D LiDAR. The number of studies on autonomous movement for robots using only visual sensors is relatively small, but this type of approach is effective at reducing the cost of sensing devices per robot. To reduce the number of external sensors required for autonomous movement, this paper proposes a novel visual navigation scheme using only a monocular camera as an external sensor. The key concept of the proposed scheme is to select a target point in an input image toward which a robot can move based on the results of semantic segmentation, where road following and obstacle avoidance are performed simultaneously. Additionally, a novel scheme called virtual LiDAR is proposed based on the results of semantic segmentation to estimate the orientation of a robot relative to the current path in a traversable area. Experiments conducted during the course of the Tsukuba Challenge 2019 demonstrated that a robot can operate in a real environment containing several obstacles, such as humans and other robots, if correct results of semantic segmentation are provided.

Automatic Generation of Multidestination Routes for Autonomous Wheelchairs

To solve the problem of autonomously navigating multiple destinations, which is one of the tasks in the Tsukuba Challenge 2019, this paper proposes a method for automatically generating the optimal travel route based on costs associated with routes. In the proposed method, the route information is generated by playing back the acquired driving data to perform self-localization, and the self-localization log is stored. In addition, the image group of road surfaces is acquired from the driving data. The costs of routes are generated based on texture analysis of the road surface image group and analysis of the self-localization log. The cost-added route information is generated by combining the costs calculated by the two methods, and by assigning the combined costs to the route. The minimum-cost multidestination route is generated by conducting a route search using cost-added route information. Then, we evaluated the proposed method by comparing it with the method of generating the route using only the distance cost. The results confirmed that the proposed method generates travel routes that account for safety when the autonomous wheelchair is being driven.

Development of Edge-Node Map Based Navigation System Without Requirement of Prior Sensor Data Collection

In this study, a novel framework for autonomous robot navigation system is proposed. The navigation system uses an edge-node map, which is easily created from electronic maps. Unlike a general self-localization method using an occupancy grid map or a 3D point cloud map, there is no need to run the robot in the target environment in advance to collect sensor data. In this system, the internal sensor is mainly used for self-localization. Assuming that the robot is running on the road, the position of the robot is estimated by associating the robot’s travel trajectory with the edge. In addition, node arrival determination is performed using branch point information obtained from the edge-node map. Because this system does not use map matching, robust self-localization is possible, even in a dynamic environment.

Path Planning in Outdoor Pedestrian Settings Using 2D Digital Maps

In this article, a framework for planning sidewalk-wise paths in data-limited pedestrian environments is presented by visually recognizing city blocks in 2D digital maps (e.g., Google Maps, and OpenStreet Maps) using contour detection, and by then applying graph theory to infer a pedestrian path from start to finish. Two main problems have been identified; first, several locations worldwide (e.g., suburban / rural areas) lack recorded data on street crossings and pedestrian walkways. Second, the continuous process of recording maps (i.e., digital cartography) is, to our current knowledge, manual and has not yet been fully automated in practice. Both issues contribute toward a scaling problem, in which the continuous monitoring and recording of such data at a global scale becomes time and effort consuming. As a result, the purpose of this framework is to produce path plans that do not depend on pre-recorded (e.g., using simultaneous localization and mapping (SLAM)) or data-rich pedestrian maps, thus facilitating navigation for mobile robots and people with visual impairment. Assuming that all roads are crossable, the framework was able to produce pedestrian paths for most locations where data on sidewalks and street crossings were indeed limited at 75% accuracy in our test-set, but certain challenges still remain to attain higher accuracy and to match real-world settings. Additionally, we describe certain works in the literature that describe how to utilize such path plans effectively.

Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time

In this study, we propose a navigation system that guides a robot at a location visited for the first time, without developing a map in advance. First, it estimates the position of a path that exists on the local map by matching the metric route information and the local map generated by simultaneous localization and mapping (SLAM); this is achieved by using a particle filter. Then, the robot travels to the destination along the estimated route. In this system, the geometric accuracy of the route information specified in advance and the accuracy of the map generated by SLAM are essential. Furthermore, it is necessary to recognize the traversable area. The experiment performed verifies the matching of the route information and local map. In the autonomous running experiment, we conduct a trial run on a course set up at the University of Tsukuba.