Structure-based vision-laser matching

Persistent merging of maps created by different sensor modalities is an insufficiently addressed problem. Current approaches either rely on appearance-based features which may suffer from lighting and viewpoint changes or require pre-registration between all sensor modalities used. This work presents a framework using structural descriptors for matching LIDAR point-cloud maps and sparse vision keypoint maps. The matching algorithm works independently of the sensors' viewpoint and varying lighting and does not require pre-registration between the sensors used. Furthermore, we employ the approach in a novel vision-laser map-merging algorithm. We analyse a range of structural descriptors and present results of the method integrated within a full mapping framework. Despite the fact that we match between the visual and laser domains, we can successfully perform map-merging using structural descriptors. The effectiveness of the presented structure-based vision-laser matching is evaluated on the public KITTI dataset and furthermore demonstrated on a map merging problem in an industrial site.

Authors

• Abel Gawel
• Titus Cieslewski
• Renaud Dubé
• Mike Bosse
• Roland Siegwart
• Juan I. Nieto 0001

Keywords

• Laser radar
• Visualization
• Three-dimensional displays
• Cameras
• Robot vision systems
• Description Of Structure
• KITTI Dataset
• Sparse Map
• Sensor Modalities
• False Positive
• True Positive
• Reference Frame
• Density Map
• Unmanned Aerial Vehicles
• Indoor Environments
• Precision Rate
• Partial Overlap
• Matthews Correlation Coefficient
• Feature Matching
• Visual Map
• Base Map
• Lidar Data
• Odometry
• Stereo Camera
• LiDAR Sensor
• Unmanned Ground Vehicles
• Place Recognition
• Loop Closure
• Prior Registration