Adaptive Heading for Perception-Aware Trajectory Following

This paper presents an adaptive heading approach for perception awareness during trajectory following. By adapting the heading of a robot to improve the feature tracking in the current mapped environment, the accuracy in localisation can be improved. This can have a significant advantage for autonomous operations in GPS-denied environments such as subsea or in caves. The aim of the proposed approach is to position the sensor used for perception and feature tracking in such a way that it; obtains a view that contains a good observation of the previously mapped environment, face forward along the direction of travel, reduces the change in heading and view the perceived environment along the surface's estimated normals. These 4 objectives create a weighted utility function that is used to find the most beneficial heading. The benefit is a system that improves feature tracking for simultaneous localisation and mapping (SLAM) while considering the safety of the robot by being aware of its surrounding. To sense the environment, a simulated sensor is discretised to a set of vertical rays based on the vertical field of view. The vertical rays are swept 360 degrees around a position to evaluate for a new heading. This allows for the simulated sensor data from ray casting to be reused and therefore reduces the computational load to find the heading which maximises the utility function. The paper is focused on holonomic robots capable of controlling the robot's heading or sensor orientation independently from the position. We present results and evaluation in a simulated environment where we show a great improvement in the SLAM's pose estimation. In addition, we endow an autonomous underwater vehicle (AUV) with the proposed approach during field trials and present the result in two different environments.