LiDAR Point Inpainting Model Using Smoothness Loss for SLAM in Dynamic Environments

Since performing simultaneous localization and mapping in dynamic environments is a challenging problem, conventional approaches have used preprocessing to detect and then remove movable objects from images. However, those methods create many holes in the places, where the movable objects are located, reducing the reliability of the estimated pose. In this paper, we propose a model with detailed classification criteria for moving objects and point cloud restoration to handle hole generation and pose errors. Our model includes a moving object segmentation network and an inpainting network with a light detection and ranging sensor. By providing residual images to the segmentation network, the model can classify idle and moving objects. Moreover, we propose a smoothness loss to ensure that the inpainting result of the model naturally connects to the existing background. Our proposed model uses the movable object’s information in an idle state and the inpainted background to accurately estimate the sensor’s pose. To use a ground truth dataset for inpainting, we created a new dataset using the CARLA simulation environment. We use our virtual datasets and the KITTI dataset to verify our model’s performance. In a dynamic environment, our proposed model demonstrates a notable enhancement of approximately 24.7% in pose estimation performance compared to the previous method.