Binocular semantic SLAM method for automatic driving scene

An automatic driving and dual-purpose technology, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as object mismatch, parameter expression is not accurate enough, mask is not fine enough, etc., to improve accuracy and robustness , improve accuracy and robustness, and intuitive physical meaning

Pending Publication Date: 2020-12-18
ZHEJIANG UNIV
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Problems solved by technology

The disadvantage of this method is that the orientation of the vehicle can only take discrete values, and the parameter expression is not precise enough; the mask generated by the three-dimensional bounding box is not fine enough, which is more likely to cause object mismatch; all objects are regarded as dynamic objects, and when the camera is tracking Unable to utilize information from static objects

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  • Binocular semantic SLAM method for automatic driving scene
  • Binocular semantic SLAM method for automatic driving scene
  • Binocular semantic SLAM method for automatic driving scene

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Embodiment

[0108]In order to further demonstrate the implementation effect of the present invention, the present invention selects 8 scenes with more dynamic vehicles from the raw data of KITTI for testing. The test uses the color binocular images provided by the data set, because KITTI does not provide the original Ground Truth of the data, this embodiment uses the same method as in KITTI to project the pose generated by the high-precision inertial navigation data to the corrected left-eye color camera coordinate system as the ground truth. In order to make the object matching more stable, the number of feature points extracted by ORB-SLAM2 and the present invention are both set to 5000 in the test. Since the loopback detection will reduce the accumulated error, in order to more accurately evaluate the influence of the method of the present invention on the error, the loopback detection is turned off for testing during the evaluation. As a result, the evaluation tool of Grupp et al. was used ...

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Abstract

The invention discloses a binocular semantic SLAM method for an automatic driving scene, and the method can detect and track other vehicles, pedestrians and other objects in a scene while enabling a vehicle to accurately track the pose of the vehicle in the environment in an automatic driving scene. According to the method, the quadric surface is used as the expression of a three-dimensional object, and compared with the prior art that the quadric surface can only be used for mapping or for a road sign for a static object, the quadric surface can be used for expressing the states of objects atdifferent moments no matter for the static object in a scene or a moving object. For the situation that semantic object observation is insufficient, the neural network and priori information are fully utilized for constraint, an error term for object observation is constructed, and nonlinear optimization solving of object state parameters is achieved.

Description

Technical field[0001]The invention relates to the field of simultaneous localization and map construction (SLAM), and in particular to a binocular semantic SLAM method for automatic driving scenarios.Background technique[0002]Simultaneous Localization and Mapping (SLAM) is one of the hot research topics in recent years. It refers to the use of equipment equipped with specific sensors to process the information received by the sensors in an unknown environment to estimate their own The location and orientation in the environment, as well as the algorithm to build a map of the environment in a specific format. As mobile robots, drones and other equipment increasingly appear in industrial and household environments, the field of augmented reality is booming, and more emerging technologies such as autonomous driving are in the ascendant, SLAM technology is one of the key algorithms in these fields. First, it has received more and more attention from industry and academia. Commonly used ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/082G06V20/41G06V20/56G06V10/25G06N3/045G06F18/2415
Inventor 章国锋鲍虎军徐鹏飞
Owner ZHEJIANG UNIV
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