Click Here for Abhyast Phase V Problem Statement


Many researchers have proposed the use of Micro Air Vehicles (MAVs) as a promising alternative to ground robot platforms for rescue tasks and a host of other applications because of their reach. MAVs are already being used in several military and civilian domains, including surveillance operations, weather observation, disaster relief coordination, and civil engineering inspections

Key Challenges

In the ground robotics domain, many algorithms exist for accurate localization in large-scale environments; however, these algorithms are usually deployed on slow moving robots, which cannot handle even moderately rough terrain. MAVs face a number of unique challenges that make developing algorithms for them far more difficult than their indoor ground robot counterparts.

  • Limited Payload: Limited payload reduces the computational power available onboard, and eliminates popular sensors such as SICK laser scanners, large-aperture cameras and high-fidelity IMUs.
  • Indirect Position Estimates: While MAVs will generally have an IMU, double-integrating acceleration measurements from lightweight MEMS IMUs results in prohibitively large position errors.
  • Fast Dynamics: MAVs have fast and unstable dynamics which result in a host of sensing, estimation, control and planning implications for the vehicle. Furthermore, MAVs such as our quadrotor are well-modelled as un-damped when operating in the hover regime.
  • Constant Motion: Unlike ground vehicles, a MAV cannot simply stop and perform more sensing or computation when its state estimates have large uncertainties. Instead, the vehicle is likely to be unable to estimate its position and velocity accurately, and as a result, it may pick up speed or oscillate, degrading the sensor measurements further.

There are further challenges that we do not address in this work such as building and planning in 2D representations of the environment.

Division of work:

  1. Quadcopter Stabilization
  2. Decoding Ardupilot
  3. Data communication
  4. Laser Scanner / SLAM implementation
  5. Video capturing


This work was part of the Boeing externship program. We are grateful for their support. The authors wish to thank the following people for their support in the project: Dr. Shantanu Bhattacharya provided all the support and guidance throughout the course of the project; Dr. Sudhir Kamle guided and mentored with the development and maintenance of the hardware.

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