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However, being passive sensors, they rely on external illumination from the environment which means that their performance degrades in low-light conditions.In this paper, we present and investigate four methods to enhance images under challenging night conditions.
Excellent introduction and survey of visual SLAM are by Younes et al.
, and those for visual odometry are by Scaramuzza and Fraundorfer .
We introduce four different image preprocessing techniques and study their performance on three systems: Although our primary focus is on feature-based visual odometry and visual SLAM systems, we additionally study the performance of presented preprocessing techniques on an object detector based on a deep convolutional neural network.
Luckily, our findings are applicable to a wide range of computer vision tasks, such as image stitching, registration and recognition.
Utilizing active sensors and other methods (such as LIDARs) is necessary to provide functionality under these conditions.
However, expanding the operation range of passive cameras through image enhancement approaches is compelling to maintain sensor redundancy under these conditions, which is a factor to functional safety.
In either case, image enhancement in this case can provide more unique descriptors and result in more reliable matching.
Such algorithms can be operating normally with no preprocessing under typical lighting conditions.
Prior work focusing on achieving operation of feature-based vision algorithms at night is limited, which motivated this work.
LIDARs are active sensors and can be used to perform the localization tasks at night, as in the work by Dong and Barfoot .