SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

TL;DR

This paper introduces a real-time, segmentation-based framework for jointly calibrating spatial and temporal parameters between LIDAR and camera sensors, improving sensor fusion accuracy in autonomous driving.

Contribution

It presents a novel joint calibration method that estimates geometric and temporal parameters simultaneously without requiring calibration labels, using semantic segmentation and optical flow.

Findings

Achieves accurate real-time calibration on KITTI dataset

Eliminates need for calibration rigs and offline processes

Enhances sensor fusion performance in dynamic environments

Abstract

With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics. Camera and LIDAR, with complementary semantic and depth information, are the typical choices for detection tasks in complicated driving environments. For most camera-LIDAR fusion algorithms, however, the calibration of the sensor suite will greatly impact the performance. More specifically, the detection algorithm usually requires an accurate geometric relationship among multiple sensors as the input, and it is often assumed that the contents from these sensors are captured at the same time. Preparing such sensor suites involves carefully designed calibration rigs and accurate synchronization mechanisms, and the preparation process is usually done offline. In this work, a segmentation-based framework is proposed to jointly estimate the geometrical and temporal parameters in the calibration of a camera-LIDAR suite. A semantic segmentation mask is first applied to both sensor modalities, and the calibration parameters are optimized through pixel-wise bidirectional loss. We specifically incorporated the velocity information from optical flow for temporal parameters. Since supervision is only performed at the segmentation level, no calibration label is needed within the framework. The proposed algorithm is tested on the KITTI dataset, and the result shows an accurate real-time calibration of both geometric and temporal parameters.