Method and system for fully automatically exploring unknown space and establishing map
A fully automatic, global map technology, applied in the field of machine learning, can solve problems such as low automation, inaccurate positioning, and low map accuracy, and achieve the effects of simple operation, saving human resources, and high accuracy
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Embodiment 1
[0036] Such as Figures 1 to 2 As shown, a preferred embodiment of a method for fully automatic exploration of unknown space and building a map of the present invention includes:
[0037] Collect stereoscopic video data using stereoscopic video acquisition tools;
[0038] Use the high-precision positioning algorithm to locate the current position in real time and build a map, and perform verification and closed-loop detection during the process of building the map;
[0039] The sub-maps are stitched together, and the loop detection is performed to obtain the final map.
[0040] In this embodiment, a stereoscopic video acquisition tool is used to collect stereoscopic video data, and then a high-performance embedded computer is used to use a high-precision positioning algorithm to locate the current position in real time and establish a map. Test, and closed-loop detection, improve positioning accuracy and map accuracy.
[0041] Positioning, map building and loop closure dete...
Embodiment 2
[0044] A preferred embodiment of a method for fully automatic exploration of unknown space and establishment of a map of the present invention, comprising:
[0045] LiDAR / stereo video sensor for collecting stereo video data;
[0046] The positioning unit uses a high-precision positioning algorithm to locate the current position in real time and establish a map, and perform verification and closed-loop detection during the process of establishing the map;
[0047] The sub-map splicing unit is used for splicing the sub-maps together for loop detection to obtain the final map.
[0048] Further, the lidar / stereo video sensor is supported by a chassis, the chassis is a drivable driving wheel or a track, and the chassis is powered by a battery.
[0049] Such as image 3 As shown, the system in this embodiment builds a map based on stereoscopic video or lidar data, and autonomous movement is based on a movable chassis. And the algorithm itself needs computer hardware support.
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