Outdoor SLAM Using Monocular Vision-Based Localization with LIDAR-Aided Mapping for service robot in Highway

This paper designed an intelligent service robot system in highway based on multi-sensor fusion. The mobile robot attempts to fuse the lidar information and monocular vision information to estimate the pose of itself and obtain an environmental map. It adapts a new SLAM method which combines lidar and vision information. Lidar is used to obtain the 2D occupancy grid map and the monocular vision SLAM algorithm uses the Extended Kalman Filter (EKF) to magnify the pose estimation. The 3-DOF pose provided by lidar is obtained through Cartographer algorithm and the monocular vision SLAM who offers the 6-DOF pose is realized with ORB-SLAM. The experimental results show that the system is effective in application as an intelligent service robot of highway.

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

Purpose Sonar sensor-based mobile robot mapping is an efficient and low cost technique for the application such as localization, autonomous navigation, SLAM and path planning. In multi-robots system, numbers of sonar sensors are used and the sound waves from sonar are interacting with the sound wave of other sonar causes wave interference. Because of wave interference, the generated sonar grid maps get distorted which resulted in decreasing the reliability of mobile robot’s navigation in the generated grid maps. This research study focus in removing the effect of wave interfaces in the sonar mapping to achieve robust navigation of mobile robot. Design/methodology/approach The wrong perception (occupancy grid map) of the environment due to cross talk/wave interference is eliminated by randomized the triggering time of sonar by varying the delay/sleep time of each sonar sensor. A software-based approach randomized triggering technique (RTT) is design in laboratory virtual instrument engineering workbench (LabVIEW) that randomized the triggering time of the sonar sensor to eliminate the effect of wave interference/cross talk when multiple sonar are placed in face-forward directions. Findings To check the reliability of the RTT technique, various real-world experiments are perform and it is experimentally obtained that 64.8% improvement in terms of probabilities in the generated occupancy grid map has been attained when compared with the conventional approaches. Originality/value This proposed RTT technique maybe implementing for SLAM, reliable autonomous navigation, optimal path planning, efficient robotics vision, consistent multi-robotic system, etc.

Building semantic grid maps for domestic robot navigation

This article proposes a semantic grid mapping method for domestic robot navigation. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks conveniently. Semantic grid maps, enhancing the occupancy grid map with the semantics of objects and rooms, endowing the robots with the capacity of robust navigation skills and human-friendly operation modes, are thus proposed to overcome this limitation. In our method, an object semantic grid map is built with low-cost sonar and binocular stereovision sensors by correctly fusing the occupancy grid map and object point clouds. Topological spaces of each object are defined to make robots autonomously select navigation destinations. Based on the domestic common sense of the relationship between rooms and objects, topological segmentation is used to get room semantics. Our method is evaluated in a real homelike environment, and the results show that the generated map is at a satisfactory precision and feasible for a domestic mobile robot to complete navigation tasks commanded in natural language with a high success rate.