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A modification to the streaming bundle adjustment algorithm is described which finds overlap in frames which are not adjacent in time to correct for error. This algorithm also addresses the loop-closing problem that occurs when the sensor returns to the starting point of a survey. Flight data from a sensor constructed with low cost, commercial off-the-shelf parts is used to demonstrate how 3D scene reconstruction using this algorithm can correct for errors compared with data gathered from a full-scale aircraft. Examples of the resulting TDSMs are presented.
Efficiently and effectively processing LiDAR data at high speeds is a difficult problem that must be addressed if autonomous vehicles are to travel at speeds as high as their manned counterparts (i.e. over 80mph for ground vehicles on highways). Many processing algorithms avoid this "high throughput" problem by using a sensor with less spatial resolution or a more powerful (and more expensive) processor. This research is intended to help individuals who do not have these options when designing their obstacle detection pipeline.
The challenge of processing LiDAR data as quickly and efficiently as possible was addressed with the recently developed event map and importance map. These are images created from LiDAR scans that use biology-inspired principles to highlight areas in a scene that can be classified as obstacles. However, the importance map has three main flaws: there is no distinction among types of object movement, the output is extremely noisy, and static object tracking does not work well at high speeds.
This research reduces these three flaws by: implementing the constant-angle principle to identify motion towards the ego vehicle, using a recursive filter to remove noise, and deriving a new static object tracking algorithm to have consistent static object tracking. After implementing these changes, the new and old importance maps are compared using LiDAR data from the KITTI dataset. The importance maps are thresholded to create obstacle masks. Through comparison of true positive and false positive rates, the new importance map shows significant improvement over the previous implementation.
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