We compare the performance of state-of-the-art person detectors for 2D range data, 3D lidar, and RGB-D data as well as selected combinations thereof in a challenging industrial use-case.
Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance.
Autonomous Driving Robotics
Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research.
Ranked #1 on 3D Human Pose Estimation on 3D Poses in the Wild Challenge (MPJPE metric)
Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics.
Furthermore, as the image space is decoupled from the heatmap space, the network can learn to reason about joints beyond the image boundary.
Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field.
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together.
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important.
In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation.
Ranked #38 on 3D Human Pose Estimation on Human3.6M
Occlusion is commonplace in realistic human-robot shared environments, yet its effects are not considered in standard 3D human pose estimation benchmarks.
Ranked #40 on 3D Human Pose Estimation on Human3.6M
We introduce and show preliminary results of a fast randomized method that finds a set of K paths lying in distinct homotopy classes.