Implementation of the Correction Algorithm in an Environment with Dynamic Actors

Abstract

<div>Safe navigation of an autonomous vehicle (AV) requires a fast and correct perception of its driving environment. Meaning, the AV needs to persistently detect and track moving objects around it with high accuracy for safe navigation. These tasks of detection and tracking are performed by the AV perception system that utilizes data from sensors such as LIDARs, radars, and cameras. The majority of AVs are typically fitted with multiple sensors to create redundancy and avoid dependence on a single sensor. This strategy has been shown to yield accurate perception results when the sensors work well and are calibrated correctly. However, over time, the cumulative use of the AV or poor placement of sensors may lead to faults that need correcting. This article proposes an online algorithm that corrects the faulty perception of an AV by determining a set of transformations that would align a cluster of measurements, from a moving vehicle in the scene to a corresponding detection in an image taken by the synchronized, forward-facing camera of the AV. The correction algorithm is first tested, assuming the availability of ground truth information to correct the LIDAR, and then tested with camera images which are used to determine ground truth. The comparison metric between expected and optimal parameters is the mean absolute error (MAE). The translation, scale, and orientation errors between the expected and optimal parameters when using ground truth data in the correction algorithm are 9.41 × 10<sup>–4</sup> m, 3.84 × 10<sup>–7</sup>, and 3.82 × 10<sup>–2</sup> degrees, respectively; and the errors for camera images are 0.414 m, 0.017, and 0.007 degrees, respectively.</div>