You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.

Cited Abstract(s):
[1]: This study describes the design and implementation of several bioinspired algorithms for providing guidance to an ultra-lightweight micro-aerial vehicle (MAV) using a 2.6 g omnidirectional vision sensor. Using this visual guidance system we demonstrate autonomous speed control, centring, and heading stabilisation on board a 30 g MAV flying in a corridorlike environment. In addition to the computation of wide-field optic flow, the comparatively high-resolution omnidirectional imagery provided by this sensor also offers the potential for image-based algorithms such as landmark recognition to be implemented in the future.
More similar to our research, implemented an efficient optic flow algorithm on a small lightweight (2 g) omnidirectional camera system on a 30 g helicopter [1] . With a ring of 8 low-resolution image chips (64 x 64 pixels), the MAV could compute optical flow. It did this by computing the edges, compressing the images and calculate the displacement by block matching which resulted in translational optical flow. The vision calculations where done on-board the helicopter with 10 Hz, yet the flight controls where computed off-board. Although the potential of a full on-board implementation is there, the redundancy lies in the ratio of cameras to sensed variables. One camera has the potential of detecting flow in 3 directions; they used 8 to only detect 2 (forward and sideways velocity).