This article explores the capabilities and limitations of lidar, cameras and radar, to provide a clear understanding of why LiDAR has emerged as a strong contender in computer vision tech race.
From Outsight by Kevin Vincent.
Unique Perspectives: How Diverse Sensors See the World
Before delving into the relative strengths and weaknesses of these technologies, let’s first provide a brief overview of how cameras, radars, and LiDAR systems operate and perceive the world around them.
Active vs. Passive Sensors
Generally speaking, sensors are devices that measure physical properties and transform them into signals suitable for processing, displaying, or storing.
Radar and LiDAR are Active sensors, different from Cameras, that are Passive.
Active sensors emit energy (e.g. Radio Waves or Laser light) and measure the reflected or scattered signal, while passive sensors detect the natural radiation or emission from the target or the environment (e.g. Sunlight or artificial light for Cameras).
Each sensing modality has its advantages and inconveniences.
As active sensors generate their own signals, external lighting conditions do not affect them. Both Radar and LiDAR function perfectly in total darkness and in direct sunlight, which is not the case for Cameras.
Beyond night vision, the impact of external lighting on cameras has far-reaching consequences. For instance, Computer Vision algorithms may fail in areas with shadows caused by objects (moving or static e.g. trees or buildings) and even in indoor settings when lighting conditions change (e.g., a door opening adding more light to the scene).
Weather conditions can significantly affect passive sensors since their sensing doesn’t directly interact with physical phenomena like rain or fog. Instead, they can only work with the resulting image, making them more susceptible to weather-related limitations. However, active sensors can also be affected by adverse conditions, depending on their wavelength.
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