Most existing robotic datasets capture static scene data and thus are limited in evaluating robots’ dynamic performance. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD (Tsinghua University Dynamic) robotic dataset, for training and evaluating their dynamic scene understanding algorithms. Specifically, the THUD dataset construction is first detailed, including organization, acquisition, and annotation methods. It comprises both real-world and synthetic data, collected with a real robot platform and a physical simulation platform, respectively. Our current dataset includes 13 larges-scale dynamic scenarios, 90K image frames, 20M 2D/3D bounding boxes of static and dynamic objects, camera poses, and IMU. The dataset is still continuously expanding. Then, the performance of mainstream indoor scene understanding tasks, e.g. 3D object detection, semantic segmentation, and robot relocalization, is evaluated on our THUD dataset. These experiments reveal serious challenges for some robot scene understanding tasks in dynamic scenes. By sharing this dataset, we aim to foster and iterate new mobile robot algorithms quickly for robot actual working dynamic environment, i.e. complex crowded dynamic scenes.

Safe and efficient mobility is vital for mobile robots navigating long-range indoor crowd environments, such as supermarkets, restaurants, and railway stations. Traditional path planning methods are challenged because of the high dynamics of pedestrians and constrained feasible regions. Existing long-range deep reinforcement learning (DRL) path planning methods often exhibit low success rates and driving speeds in long-range navigation tasks under crowded conditions. To overcome these issues, we propose a new two-stage DRL method, known as TSDRL, where the long-range navigation task is divided into subgoal generation (SG) and planning refinement (PR) stages. In the SG stage, the agent is trained to learn a decision-making policy to generate subgoals at each decision time to avoid dense crowds. In the PR stage, the agent learns a safer and more efficient planning policy based on each subgoal generated in the SG stage to improve the robot’s movement safety and speed. Simulated experiments show that our method outperforms traditional and long-range DRL path planning methods in terms of safety, efficiency, generalization, and robustness. Furthermore, we evaluate our approach using the Turtlebot2 platform in a real-world setting, demonstrating that the robot can navigate safely and efficiently while avoiding dense crowds.