Oscillations make a self-scaled model for honeybees’ visual odometer reliable regardless of flight trajectory

Honeybees foraging and recruiting nest-mates by performing the waggle dance need to be able to gauge the flight distance to the food source regardless of the wind and terrain conditions. Previous authors have hypothesized that the foragers’ visual odometer mathematically integrates the angular velocity of the ground image sweeping backward across their ventral viewfield, known as translational optic flow. The question arises as to how mathematical integration of optic flow (usually expressed in radians/s) can reliably encode distances, regardless of the height and speed of flight. The vertical self-oscillatory movements observed in honeybees trigger expansions and contractions of the optic flow vector field, yielding an additional visual cue called optic flow divergence. We have developed a self-scaled model for the visual odometer in which the translational optic flow is scaled by the visually estimated current clearance from the ground. In simulation, this model, which we have called SOFIa, was found to be reliable in a large range of flight trajectories, terrains and wind conditions. It reduced the statistical dispersion of the estimated flight distances approximately 10-fold in comparison with the mathematically integrated raw optic flow model. The SOFIa model can be directly implemented in robotic applications based on minimalistic visual equipment.

Stereo vision-based visual odometry for planetary exploration

The ability to localize an unmanned vehicle is an essential requirement for extraterrestrial robotic exploration missions. The goal of this thesis is to develop a visual odometry algorithm capable of operating in real-time and in natural unstructured environments. Accuracy, repeatability and computational cost were the primary considerations during the development of the algorithm. The resulting visual odometry algorithm can operate in real-time and provides the foundations for further development. More commonly used approaches for localization include the use of inertial measurement units (IMU) or wheel odometry, which are prone to drift and slippage respectively, making them unreliable for long duration missions. Visual odometry also experiences error accumulation, however, it offers the possibility of mitigating this problem through techniques such as loop closing and bundle adjustment. The performance of the Iterative Closest Point (ICP) algorithm in conjunction within the visual odometer was also evaluated and shown to have improved overall localization performance.

Stereo vision-based visual odometry for planetary exploration

The ability to localize an unmanned vehicle is an essential requirement for extraterrestrial robotic exploration missions. The goal of this thesis is to develop a visual odometry algorithm capable of operating in real-time and in natural unstructured environments. Accuracy, repeatability and computational cost were the primary considerations during the development of the algorithm. The resulting visual odometry algorithm can operate in real-time and provides the foundations for further development. More commonly used approaches for localization include the use of inertial measurement units (IMU) or wheel odometry, which are prone to drift and slippage respectively, making them unreliable for long duration missions. Visual odometry also experiences error accumulation, however, it offers the possibility of mitigating this problem through techniques such as loop closing and bundle adjustment. The performance of the Iterative Closest Point (ICP) algorithm in conjunction within the visual odometer was also evaluated and shown to have improved overall localization performance.

A robust iterative pose tracking method assisted by modified visual odometer

Purpose The purpose of this paper is to propose a robust iterative LIDAR-based pose tracking method assisted by modified visual odometer to resist initial value disturbance and locate a robot in the environments with certain occlusion. Design/methodology/approach At first, an iterative LIDAR-based pose tracking method is proposed. The LIDAR information is filtered and occupancy grid map is pre-processed. The sample generation and scoring are iterated so that the result is converged to the stable value. To improve the efficiency of sample processing, the integer-valued map indices of rotational samples are preserved and translated. All generated samples are analyzed to determine the maximum error direction. Then, a modified visual odometer is introduced for error compensation. The oriented fast and rotated brief (ORB) features are uniformly sampled in the image. A local map which contains key frames for reference is maintained. These two measures ensure that the modified visual odometer is able to return robust result which compensates the error of LIDAR-based pose tracking method in the maximum error direction. Findings Three experiments are conducted to prove the advantages of the proposed method. The proposed method can resist initial value disturbance with high computational efficiency, give back credible real-time result in the environment with abundant features and locate a robot in the environment with certain occlusion. Originality/value The proposed method is able to give back real-time pose tracking results with robustness. The iterative sample generation enables the robot to resist initial value disturbance. In each iteration, rotational and translational samples are separately generated to enhance computational efficiency. The maximum error direction of LIDAR-based pose tracking method is determined by principle component analysis and compensated by the result of modified visual odometer to give back correct pose in the environment with certain occlusion.