High altitude stereo visual odometry Stereo visual odometry has received little investigation in high altitude applications due to the generally poor performance of rigid stereo rigs at extremely small baselinetodepth ratios. Without additional sensing, metric scale is considered lost and odometry is seen as effective only for monocular perspectives. This paper presents a novel modification to stereo based visual odometry that allows accurate, metric pose estimation from high altitudes, even in the presence of poor calibration and without additional sensor inputs. By relaxing the (typically fixed) stereo transform during bundle adjustment and reducing the dependence on the fixed geometry for triangulation, metrically scaled visual odometry can be obtained in situations where high altitude and structural deformation from vibration would cause traditional algorithms to fail. This is achieved through the use of a novel constrained bundle adjustment routine and accurately scaled pose initializer. We present visual odometry results demonstrating the technique on a shortbaseline stereo pair inside a fixedwing UAV flying at significant height (30100m).

Hybrid IMUAided Approach for Optimized Visual Odometry Autonomous navigation of unmanned vehicles in GPSdenied environments is a challenging problem, especially for small ground vehicles and micro aerial vehicles (MAVs) which are characterized by their small payload, short battery lifetime and limited processing resources. Stereo vision positioning has been introduced as a scalefree positioning technique, but it is computationally expensive. Monocular vision systems aided by inertial measurement unit (IMU) are more computationally efficient but it suffers from IMU random biases and scale errors. In this paper, we propose a hybrid visualinertial odometry solution that minimizes the computation load by dividing the mission into two interchangeable stages. Firstly, a stereo vision stage in which a loosely coupled integration between stereo cameras and IMU is performed. In this stage, an extended Kalman filter (EKF) is used to automatically and dynamically estimate IMU biases. Once the IMU is calibrated, a monocular stage is activated where the system is downgraded into single camera getting the motion scale from the calibrated IMU. The proposed solution has been tested using the popular IMUenabled ZEDMini tracking camera. We compared our stereo vision solution against the IMUaided monocular solution and the results showed accurate positioning with the advantage of less computation. Further analysis is provided where we compared our solution with the builtin solutions of the ZED Mini camera and the Intel Realsense T265 tracking camera.