5 research outputs found
Domain and Modality Gaps for LiDAR-based Person Detection on Mobile Robots
Person detection is a crucial task for mobile robots navigating in
human-populated environments and LiDAR sensors are promising for this task,
given their accurate depth measurements and large field of view. This paper
studies existing LiDAR-based person detectors with a particular focus on mobile
robot scenarios (e.g. service robot or social robot), where persons are
observed more frequently and in much closer ranges, compared to the driving
scenarios. We conduct a series of experiments, using the recently released
JackRabbot dataset and the state-of-the-art detectors based on 3D or 2D LiDAR
sensors (CenterPoint and DR-SPAAM respectively). These experiments revolve
around the domain gap between driving and mobile robot scenarios, as well as
the modality gap between 3D and 2D LiDAR sensors. For the domain gap, we aim to
understand if detectors pretrained on driving datasets can achieve good
performance on the mobile robot scenarios, for which there are currently no
trained models readily available. For the modality gap, we compare detectors
that use 3D or 2D LiDAR, from various aspects, including performance, runtime,
localization accuracy, robustness to range and crowdedness. The results from
our experiments provide practical insights into LiDAR-based person detection
and facilitate informed decisions for relevant mobile robot designs and
applications
From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art
Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments