Semi-dense visual-inertial odometry and mapping for computationally constrained platforms

Wenxin Liu, Kartik Mohta, Giuseppe Loianno, Kostas Daniilidis, Vijay Kumar

Research output: Contribution to journalArticlepeer-review


In this paper we present a direct semi-dense stereo Visual-Inertial Odometry (VIO) algorithm enabling autonomous flight for quadrotor systems with Size, Weight, and Power (SWaP) constraints. The proposed approach is validated through experiments on a 250 g, 22 cm diameter quadrotor equipped with a stereo camera and an IMU. Semi-dense methods have superior performance in low texture areas, which are often encountered in robotic tasks such as infrastructure inspection. However, due to the measurement size and iterative nonlinear optimization, these methods are computationally more expensive. As the scale of the platform shrinks down, the available computation of the on-board CPU becomes limited, making autonomous navigation using optimization-based semi-dense tracking a hard problem. We show that our direct semi-dense VIO performs comparably to other state-of-the-art methods, while taking less CPU than other optimization-based approaches, making it suitable for computationally-constrained small platforms. Our method takes less amount of CPU than the state-of-the-art semi-dense method, VI-Stereo-DSO, due to a simpler framework in the algorithm and a multi-threaded code structure allowing us to run real-time state estimation on an ARM board. With a low texture dataset obtained with our quadrotor platform, we show that this method performs significantly better than sparse methods in low texture conditions encountered in indoor navigation. Finally, we demonstrate autonomous flight on a small platform using our direct semi-dense Visual-Inertial Odometry. Supplementary code, low texture datasets and videos can be found on our github repo:

Original languageEnglish (US)
Pages (from-to)773-787
Number of pages15
JournalAutonomous Robots
Issue number6
StatePublished - Sep 2021


  • Micro Aerial Vehicles
  • Photometric Error
  • Visual-Inertial Odometry

ASJC Scopus subject areas

  • Artificial Intelligence


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