Deep regression for LiDAR-based localization in dense urban areas

Abstract LiDAR-based localization in a city-scale map is a fundamental question in autonomous driving research. As a reasonable localization scheme, the localization can be performed by global retrieval (that suggests potential candidates from the database) followed by geometric registration (that obtains an accurate relative pose). In this work, we develop a novel end-to-end, deep multi-task network that simultaneously performs global retrieval and geometric registration for LiDAR-based localization. Both retrieval and registration are formulated and solved as regression problems, and they can be deployed independently during inference time. We also design two mechanisms to enhance our multi-task regression network’s performance: residual connections for point clouds and a new loss function with learnable parameters. To alleviate the common phenomenon of vanishing gradients in neural networks, we employ residual connections to support constructing a deeper network effectively. At the same time, to solve the problem of huge differences in scale and units between different tasks, we propose a loss function that can automatically balance multi-tasks. Experiments on two public benchmarks validate the state-of-the-art performance of our algorithm in large-scale LiDAR-based localization.