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This article mainly introduces the relevant knowledge of what the Apollo3.5 perception module is, the content is detailed and easy to understand, the operation is simple and fast, and has a certain reference value. I believe you will gain something after reading this Apollo3.5 perception module. Let's take a look at it.

Brief introduction

The Apollo awareness module has the following new features:

Support for VLS- 128 line lidar

Using multi-camera method to detect obstacles

More advanced traffic light monitoring module

Configurable Sensor Fusion method

The perception module uses 5 cameras (2 front, 2 sides and 1 rear), 2 millimeter wave radars (front and rear), 3 16-line lidars (2 in the rear and 1 front) and 1 128-line lidar to identify obstructions. and fuse the information of each sensor to produce the final tracking list.

The obstacle detection sub-module detects, classifies and tracks obstacles. Predict both the motion and position information of the obstacle (for example, direction and speed). For the lane, the lane can be constructed by analyzing the pixels of the post-processing lane and combining the calculated relative position information with the reference vehicle (such as L0, L1, R0, R1).

System structure

The basic structure of the awareness module is as follows:

The basic structure diagram of the perception module

The detailed workflow of the awareness module is shown below.

Workflow flow chart of perception module

Input

The input of the perception module includes the following parts:

128line lidar data (cyber channel/apollo/sensor/velodyne128) 16-line lidar data (cyber channel/apollo/sensor/lidar_front,lidar_rear_left,lidar_rear_right) millimeter wave radar data (cyber channel/apollo/sensor/radar_front,radar_rear) image data (cyber channel/apollo/sensor/camera/front_6mm Front_12mm) calibrated external parameters of radar sensors (provided in YAML file format) external and internal parameters of calibrated front camera (provided in YAML file format) vehicle speed and angular velocity (cyber channel/apollo/localization/pose) output

The output of the perception module is as follows:

The output (cyber channel/apollo/perception/traffic_light) setting instruction of the 3D obstacle track (cyber channel/apollo/perception/obstacles) traffic light detection and recognition sub-module with direction, speed and classification information is set universally in the configuration file modules/perception/conf/perception_lowcost.conf. Run the command:. / scripts/bootstrap.sh to start the web graphical interface. Select the car model in the graphical user interface. Start the awareness module using the following command:. / scripts/perception_lowcost_vis.sh start or click the perception button on the Module Controller view page of the graphical user interface. The command to stop the awareness module is as follows:. / scripts/perception_lowcost_vis.sh stop Note: do not try to start the awareness module through the graphical user interface and use script commands to stop it, and vice versa. Download demo data from the Apollo Open data platform (Open Data Platform.).

Note:

If you are redirected to Baidu Cloud's landing page, please repeat step 5 above after completing the login (click the Open data platform link)

Matters needing attention

In order to run the Caffe-based awareness module, Nvidia GPU and CUDA environments are required. Apollo provides the CUDA and Caffe libraries in the published Docker image. However, the driver for Nvidia GPU is not installed in the Docker image.

To run the CUDA acceleration-based awareness module, be sure to install the same version of the Nvidia driver as the host computer in the Docker image, and then compile Apollo with the GPU option (for example, using the. / apollo.sh build_gpu or. / apollo.sh build_opt_gpu command).

Please refer to How to Run Perception Module on Your Local Computer.

This module contains a modified Caffe deep learning framework released in binary format. The copyright description of Caffe is as follows:

1 COPYRIGHT 2 3All contributions by the University of California: 4Copyright (c) 2014-2017 The Regents of the University of California (Regents) 5All rights reserved. 6 7All other contributions: 8Copyright (c) 2014-2017, the respective contributors 9All rights reserved.1011Caffe uses a shared copyright model: each contributor holds copyright over their contributions to Caffe. The project versioning records all such contribution and copyright details. If a contributor wants to further mark their specific copyright on a particular contribution, they should indicate their copyright solely in the commit message of the change when it is committed.1213LICENSE1415Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:1617 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.18 2. Redistributions in binary form must reproduce the above copyright notice This is the end of the this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.1920 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" > article on "what is the Apollo3.5 perception module?" Thank you for reading! I believe that everyone has a certain understanding of the knowledge of "what is the Apollo3.5 perception module". If you want to learn more knowledge, you are welcome to follow the industry information channel.

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