Giovanni Cioffi

Giovanni Cioffi, Titus Cieslewski, Davide Scaramuzza

Robotic practitioners generally approach the vision-based SLAM problem through discrete-time formulations. This has the advantage of a consolidated theory and very good understanding of success and failure cases. However, discrete-time SLAM needs tailored algorithms and simplifying assumptions when high-rate and/or asynchronous measurements, coming from different sensors, are present in the estimation process. Conversely, continuous-time SLAM, often overlooked by practitioners, does not suffer from these limitations. Indeed, it allows integrating new sensor data asynchronously without adding a new optimization variable for each new measurement. In this way, the integration of asynchronous or continuous high-rate streams of sensor data does not require tailored and highly-engineered algorithms, enabling the fusion of multiple sensor modalities in an intuitive fashion. On the down side, continuous time introduces a prior that could worsen the trajectory estimates in some unfavorable situations. In this work, we aim at systematically comparing the advantages and limitations of the two formulations in vision-based SLAM. To do so, we perform an extensive experimental analysis, varying robot type, speed of motion, and sensor modalities. Our experimental analysis suggests that, independently of the trajectory type, continuous-time SLAM is superior to its discrete counterpart whenever the sensors are not time-synchronized. In the context of this work, we developed, and open source, a modular and efficient software architecture containing state-of-the-art algorithms to solve the SLAM problem in discrete and continuous time.

Giovanni Cioffi, Leonard Bauersfeld, Davide Scaramuzza

Visual-inertial odometry (VIO) is widely used for state estimation in autonomous micro aerial vehicles using onboard sensors. Current methods improve VIO by incorporating a model of the translational vehicle dynamics, yet their performance degrades when faced with low-accuracy vehicle models or continuous external disturbances, like wind. Additionally, incorporating rotational dynamics in these models is computationally intractable when they are deployed in online applications, e.g., in a closed-loop control system. We present HDVIO2.0, which models full 6-DoF, translational and rotational, vehicle dynamics and tightly incorporates them into a VIO with minimal impact on the runtime. HDVIO2.0 builds upon the previous work, HDVIO, and addresses these challenges through a hybrid dynamics model combining a point-mass vehicle model with a learning-based component, with access to control commands and IMU history, to capture complex aerodynamic effects. The key idea behind modeling the rotational dynamics is to represent them with continuous-time functions. HDVIO2.0 leverages the divergence between the actual motion and the predicted motion from the hybrid dynamics model to estimate external forces as well as the robot state. Our system surpasses the performance of state-of-the-art methods in experiments using public and new drone dynamics datasets, as well as real-world flights in winds up to 25 km/h. Unlike existing approaches, we also show that accurate vehicle dynamics predictions are achievable without precise knowledge of the full vehicle state.

Giovanni Cioffi, Davide Scaramuzza

Motivated by the goal of achieving robust, drift-free pose estimation in long-term autonomous navigation, in this work we propose a methodology to fuse global positional information with visual and inertial measurements in a tightly-coupled nonlinear-optimization-based estimator. Differently from previous works, which are loosely-coupled, the use of a tightly-coupled approach allows exploiting the correlations amongst all the measurements. A sliding window of the most recent system states is estimated by minimizing a cost function that includes visual re-projection errors, relative inertial errors, and global positional residuals. We use IMU preintegration to formulate the inertial residuals and leverage the outcome of such algorithm to efficiently compute the global position residuals. The experimental results show that the proposed method achieves accurate and globally consistent estimates, with negligible increase of the optimization computational cost. Our method consistently outperforms the loosely-coupled fusion approach. The mean position error is reduced up to 50% with respect to the loosely-coupled approach in outdoor Unmanned Aerial Vehicle (UAV) flights, where the global position information is given by noisy GPS measurements. To the best of our knowledge, this is the first work where global positional measurements are tightly fused in an optimization-based visual-inertial odometry algorithm, leveraging the IMU preintegration method to define the global positional factors.