The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) g...The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.展开更多
Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural ...Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.展开更多
基金the National Natural Science Foundation of China (61720106012 and 61403215)the Foundation of State Key Laboratory of Robotics (2006-003)the Fundamental Research Funds for the Central Universities for the financial support of this work.
文摘The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.
文摘Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.
