Ranga Rodrigo

Point clouds, usually obtained using LiDARs, is an important perception manner commonplace in vision based autonomous navigation. One challenge of large-scale outdoor LiDAR point clouds is the high volume of points; generally millions of points per frame of observation. Object detection, and semantic segmentation important problems in the point-cloud domain.

Computer vision is an enabler for self-driving, whether the input is camera based-video or LIDAR-based point clouds. We are working along multiple avenues to contribute to this important area. Road marking detection and lane detection directly assist self-driving. We have introduced a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of annotation formats and lower resolutions. In SwiftLane, we presented a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. Both these were with the support from Creative Software under the supervision of Dr. Peshala Jayasekara. Inspired by recent improvements in point cloud processing for autonomous navigation, we focused on using hierarchical graph neural networks for processing and feature learning over large-scale outdoor LiDAR point clouds. In point based and GNN models for semantic segmentation with our work achieves a significant improvement for GNNs on the SemanticKITTI dataset. We have also made a more fundamental contribution in using cross-modal contrastive learning approach to learn transferable 3D point cloud representations.

Reinforcement learning algorithms have been successfully trained for games like GO, Atari, and Chess in simulated environments. However, in cue sport-based games like Carrom, real world is unpredictable unlike in Chess and GO due to the stochastic nature of the gameplay as well as the effect of external factors such as friction combined with multiple collisions. Hence, solely training in a simulated platform for games like Billiard and Carrom, which need precise execution of a shot, would not be ideal in actual gameplay. This paper presents a real-time vision based efficient robotic system to play Carrom against a proficient human opponent. We demonstrate the challenges of adopting a reinforcement learning algorithm beyond simulations in implementing strategic gameplay for the robotic system. We currently achieve an overall shot accuracy of 70.6% by combining heuristic and reinforcement learning algorithms. Analysis of the overall results suggests the possibility of adopting a realworld training for board games which need precise mechanical actuation beyond simulations.