LR Hybrid Bird's-Eye Edge Based Semantic Visual SLAM for AVP


semantics SLAM Framework to take advantage of hybrid edge The information is on the mosaic .

To extract from the mosaic edge and free-space outline , Different segmentation methods are designed to remove noisy images glare edge Edge of strong light and Twisted edges of objects ( Yes IPM Caused by the ).

Because only freespace Segmentation needs training , Our approach has been reduced labeling burden Label burden .

At the same time, it builds Semantic edge point cloud image and occupancy grid map.

1. Introduction

A combination of Unsupervised edge monitor and A group of IPM Edge segmentation method based on . Our approach only needs to be crude freespace mark .

A. Visual SLAM for Multi-Camera Systems


B. Semantic Visual SLAM in AD

We design a hybrid edge extraction method , Just rough freespace mark , 10X fast .

C. AVP Applications


3. Framework

  • Bird's-eye edge extraction: Mosaic and generated freespace The pictures are all input ; And then test raw hybrid edges, I"M Segmentation to remove most of the edge noise
  • Mapping: Generate global edge point cloud image , and occupancy grid map
  • Odometry: The current... In the local map pose It's done with semantic point cloud registration , The current transformation of the given wheel speed is the initial value

4. Bird's-eye Edge Extraction

There are a lot of mosaics road markings, It's all good information , But the image is polluted by strong light ; \

There are two sub modules , 1. raw edges testing ; 2. The result of removing noise and distortion

A. Raw Edge Detection

This can be done with traditional edge detection ( such as Canny edge); stay freespace With the help of segmentation , Remove the edges off the ground ;

B. IPM-based Edge Segmentation

One of the basic four ways to clear the edges is utilize IPM Inside distortion effect Characteristics of ; Most of the edges are stable radial Of , It's going through every camera focal point Of .

Ray-based segmentation


Line-based segmentation


Polyline-based segmentation


5. Semantic Odometry and Mapping

A. Local Map Generation

The segmentation module can't completely remove the noisy edges . meanwhile , some road markings Or the edge of the parking spot may be removed by mistake , If they happen to be in The direction of the ray On . So the current extraction edge It could be incomplete, unstable .

We built local edge map, Pictured above , The fusion result of different frames is probability .

B. Pose Estimation

First, project the current frame observation to local map ( adopt T), Data association through nearest neighbor search , Current pose :

\[\begin{aligned} \min _{\mathbf{T}_{\text {venicle }}^{\text {local }}} \underbrace{\sum_{i}\left\|\mathbf{p}_{i}^{\text {local }}-\mathbf{T}_{\text {vehicle }}^{\text {local }} \mathbf{p}_{i}^{\text {vehicle }}\right\|_{2}}_{\text {bird's-eye edge point distances }}+\\ & w \underbrace{\sum_{j}\left\|\mathbf{p}_{j}^{\text {local }}-\mathbf{T}_{\text {vehicle }}^{\text {local }} \mathbf{p}_{j}^{\text {vehicle }}\right\|_{2}}_{\text {free-space edge point distances }} \end{aligned} \]

C. Global Mapping


6. Experiments


7. Conclusions

Nothing .

Please bring the original link to reprint ,thank
Similar articles