Robot Navigation In Outdoor Environments Research Articles

ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments

ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments

DiTer: Diverse Terrain and Multimodal Dataset for Field Robot Navigation in Outdoor Environments

DiTer: Diverse Terrain and Multimodal Dataset for Field Robot Navigation in Outdoor Environments

GrASPE: Graph Based Multimodal Fusion for Robot Navigation in Outdoor Environments

GrASPE: Graph Based Multimodal Fusion for Robot Navigation in Outdoor Environments

Loop detection for 3D LiDAR SLAM using segment-group matching

Three-dimensional (3D) maps are indispensable for autonomous robot navigation in outdoor environments. Loop closure, which is a key technology in robotic mapping, is essential for creating consistent maps. This paper proposes an efficient method of loop detection for 3D-mapping. The proposed method detects loop constraints by estimating robot poses in revisited places using point-cloud registration. A difficulty in the registration is a large set of 3D points obtained by laser sensors, which requires a long processing time. To reduce the processing time, the proposed method employs a coarse-to-fine approach. Coarse estimation is performed using planes, lines, and balls instead of 3D points, and reduces the hypothesized loop constraints using geometric constraints between the segments. Subsequently, fine estimation is performed using the iterative closest points (ICP) algorithm and 3D points. Another difficulty is the precision of loop detection. To increase the precision, the proposed method employs robustification techniques such as outlier removal in the registration, combination of feature-based and pose-based methods, and robust pose adjustment. Experiments using large-scale datasets show that the proposed method realizes realtime loop detection in a variety of outdoor environments including cities, parks, and forest areas.

1P1-C06 Image Feature-based Traversability Analysis for Mobile Robot Navigation in Outdoor Environment(Vision System for Mobile Robot)

1P1-C06 Image Feature-based Traversability Analysis for Mobile Robot Navigation in Outdoor Environment(Vision System for Mobile Robot)

실외 자율 로봇 주행을 위한 센서 퓨전 시스템 구현

Autonomous guided robot navigation which consists of following unknown paths and avoiding unknown obstacles has been a fundamental technique for unmanned robots in outdoor environments. The unknown path following requires techniques such as path recognition, path planning, and robot pose estimation. In this paper, we propose a novel sensor fusion system for autonomous guided robot navigation in outdoor environments. The proposed system consists of three monocular cameras and an array of nine infrared range sensors. The two cameras equipped on the robot's right and left sides are used to recognize unknown paths and estimate relative robot pose on these paths through bayesian sensor fusion method, and the other camera equipped at the front of the robot is used to recognize abrupt curves and unknown obstacles. The infrared range sensor array is used to improve the robustness of obstacle avoidance. The forward camera and the infrared range sensor array are fused through rule-based method for obstacle avoidance. Experiments in outdoor environments show the mobile robot with the proposed sensor fusion system performed successfully real-time autonomous guided navigation.

OBJECTS SEGMENTATION USING MULTIPLE FEATURES FOR ROBOT NAVIGATION ON OUTDOOR ENVIRONMENT

This paper presents the method to recognize objects for autonomous robot navigation in outdoor environment. The proposition of the method segments from an image taken by a moving robot in an outdoor environment. The method begins with object segmentation, which uses multiple features to obtain the object of segmented region. Multiple features are color, context information, line segments, edge, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). We model the objects of outdoor environment that define their characteristics individually. We segment the region as a mixture using the proposed features and methods. Objects can be detected when we combine predefined multiple features. Next, the stage classifies the object into natural and artificial ones. We detect sky and trees of natural objects. And we detect building of artificial objects. The last stage shows the combination of appearance and context information. We implement the result of object segmentation using multiple features through experiments.

Spectral contrasts for landmark navigation

Visual robot navigation in outdoor environments would benefit from an illumination-independent representation of images. We explore how such a representation, comprising a black skyline of objects in front of a white sky, can be obtained from dual-channel spectral contrast measures. Light from sky and natural objects under different conditions of illumination was analyzed by five spectral channels: ultraviolet, blue, green, red, and near infrared. Linear discriminant analysis was applied to determine the optimal linear separation between sky and object points. A statistical comparison shows that contrasts with large differences in the wavelength of the two channels, specifically ultraviolet-infrared, blue-infrared, and ultraviolet-red, yield the best separation. Within a single channel, the best separation was obtained for ultraviolet light. The gain in separation quality when all five channels were included is relatively small.

Localization along routes, based upon iconic and Global Positioning System information in large-scale outdoor environments

This paper proposes a method of robot navigation in outdoor environments based upon panoramic view and Global Positioning System (GPS) information. Our system is equipped with a GPS navigator and a camera. The route scene can be described by three-dimensional objects extracted as landmarks from panoramic representations. For an environment having limited routes, a two-dimensional map can be made based upon routes scenes, assuming that the topological relation of routes at intersections is known. By using GPS information, the global position of a mobile robot can be known, and a coarse-to-fine method is used to generate an outdoor environment map and locate a mobile robot. First, a robot finds its approximate position based on the GPS information. Then, it identifies its location from the image information. Experimental results in outdoor environments are given.