Driving the Future: Sequential Point-cloud processing and it’s application in Autonomous Navigation

Abstract

Advancements in 3D sensing, driven by the adoption of LiDAR technology, have reignited innovation in autonomous navigation. LiDAR sensors offer real-time, large-scale point clouds, surpassing traditional vision solutions. Datasets like nuScenes and KITTI empower researchers in tasks such as Localization, Place Recognition, and Obstacle Trajectory Prediction. This thesis contributes by exploring the modeling and prediction of large-scale point cloud sequences. Additionally, it showcases a practical application, representing point-cloud sequences as occupancy grid maps and generating trajectories for autonomous navigation. This dual focus enhances our understanding of autonomous navigation complexities, providing valuable insights into real-world implementation challenges. The first study introduces ATPPNet, an innovative architecture tailored to predict future point cloud sequences using Conv-LSTM, channel-wise and spatial attention, and a 3D-CNN branch. Extensive experiments conducted on publicly available datasets demonstrate the model’s impressive performance, outperforming existing methods. The thesis includes a comprehensive ablative study of ATPPNet and an application study showcasing its potential for tasks such as odometry estimation. In the second study, the thesis proposes NeuroSMPC, a novel integration of data-driven frameworks with sampling-based optimal control for real-time applications, particularly onroad autonomous driving. The 3D-CNN layers in NeuroSMPC predicts optimal mean control without iterative resampling, reducing computation time. The approach proves effective in generating diverse control samples around the predicted optimal mean, facilitating real-time trajectory rollout in the presence of dynamic obstacles. The 3D-CNN architecture implicitly learns future trajectories of dynamic agents, ensuring collision-free navigation without explicit future trajectory predictions. Performance gains are demonstrated over multiple baselines in on-road scenes through closed-loop simulations in CARLA. Real-world applicability is showcased by deploying the system on a custom Autonomous Driving Platform (AutoDP). These studies collectively advance the understanding and implementation of autonomous navigation systems in dynamic environments.