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Package Summary
| Tags | No category tags. |
| Version | 0.1.0 |
| License | Apache 2.0 |
| Build type | AMENT_CMAKE |
| Use | RECOMMENDED |
Repository Summary
| Description | |
| Checkout URI | https://github.com/ieiauto/autodrrt.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-09-19 |
| Dev Status | UNKNOWN |
| CI status | No Continuous Integration |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Ambroise Vincent
- Xinyu Wang
Authors
lidar_apollo_segmentation_tvm_nodes
Purpose / Use cases
An alternative to Euclidean clustering. This node detects and labels foreground obstacles (e.g. cars, motorcycles, pedestrians) from a point cloud, using a neural network.
Design
See the design of the algorithm in the core (lidar_apollo_segmentation_tvm) package’s design documents.
Usage
lidar_apollo_segmentation_tvm and lidar_apollo_segmentation_tvm_nodes will not work without a neural network.
See the lidar_apollo_segmentation_tvm usage for more information.
Assumptions / Known limits
The original node from Apollo has a Region Of Interest (ROI) filter. This has the benefit of working with a filtered point cloud that includes only the points inside the ROI (i.e., the drivable road and junction areas) with most of the background obstacles removed (such as buildings and trees around the road region). Not having this filter may negatively impact performance.
Inputs / Outputs / API
Inputs
The input are non-ground points as a PointCloud2 message from the sensor_msgs package.
Outputs
The output is a DetectedObjectsWithFeature.
Parameters
{{ json_to_markdown(“perception/lidar_apollo_segmentation_tvm_nodes/schema/lidar_apollo_segmentation_tvm_nodes.schema.json”) }}
Error detection and handling
Abort and warn when the input frame can’t be converted to base_link.
Security considerations
Both the input and output are controlled by the same actor, so the following security concerns are out-of-scope:
- Spoofing
- Tampering
Leaking data to another actor would require a flaw in TVM or the host operating system that allows arbitrary memory to be read, a significant security flaw in itself. This is also true for an external actor operating the pipeline early: only the object that initiated the pipeline can run the methods to receive its output.
A Denial-of-Service attack could make the target hardware unusable for other pipelines but would require being able to run code on the CPU, which would already allow a more severe Denial-of-Service attack.
No elevation of privilege is required for this package.
Future extensions / Unimplemented parts
Related issues
- #226: Autoware.Auto Neural Networks Inference Architecture Design
Wiki Tutorials
Package Dependencies
| Deps | Name |
|---|---|
| ament_cmake | |
| ament_cmake_auto | |
| autoware_cmake | |
| ament_lint_auto | |
| autoware_lint_common | |
| ros_testing | |
| lidar_apollo_segmentation_tvm | |
| rclcpp | |
| rclcpp_components | |
| sensor_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_segmentation_tvm_nodes.launch.xml
- Copyright 2021-2023 Arm Ltd., the Autoware Foundation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: labeled_clusters]
- param_file [default: $(find-pkg-share lidar_apollo_segmentation_tvm_nodes)/config/lidar_apollo_segmentation_tvm_nodes.param.yaml]
- z_offset [default: -2.0]