Summary
Predicting where people will walk helps robots move safely around them. The authors noticed robots often use special LiDAR sensors that create maps of obstacles nearby, but they also have cameras that show detailed views of people. They designed a new system called MamMA that looks at parts of these maps and also pays attention to whether pedestrians seem aware of the robot, since this affects how they move. Using this combined information, their method can better guess where people will go next compared to other systems. Tests on different datasets showed MamMA made more accurate predictions about future pedestrian paths.
pedestrian trajectory predictionoccupancy mapLiDARegocentric viewpedestrian awarenessMamba modelmobile robotsobstacle detectiondisplacement errorhuman-robot coexistence
Authors
Juncen Long, Xiaofeng Jin, Gianluca Bardaro, Simone Mentasti, Matteo Matteucci
Abstract
Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexistence environments. Some pedestrian trajectory prediction algorithms extract information about obstacles near pedestrians from top-down view images to improve the accuracy of trajectory prediction. However, mobile robots typically create local occupancy maps using LiDAR, rather than top-down view images. Meanwhile, the vision sensors on board robots provide egocentric view images, which contain fine-grained behavioral information about the pedestrians near the robot. To better use the information collected by LiDAR and on-board vision sensors, we propose MamMA, a Mamba-based pedestrian trajectory prediction algorithm considering occupancy maps and pedestrian awareness states. MamMA divides the occupancy map by patches and extracts obstacle features from each patch to create map features. Pedestrian awareness states are divided and considered, as some studies show that awareness states affect the perception and speed of pedestrians. Furthermore, a Mamba-based model is proposed to predict the future trajectories of pedestrians based on different types of features. Experiments on the STCrowd, SiT, JRDB, ETH, and UCY datasets show that MamMA achieves better average displacement error and final displacement error than the state-of-the-art algorithms.