2 research outputs found
RH-Map: Online Map Construction Framework of Dynamic Objects Removal Based on Region-wise Hash Map Structure
Mobile robots navigating in outdoor environments frequently encounter the
issue of undesired traces left by dynamic objects and manifested as obstacles
on map, impeding robots from achieving accurate localization and effective
navigation. To tackle the problem, a novel map construction framework based on
3D region-wise hash map structure (RH-Map) is proposed, consisting of front-end
scan fresher and back-end removal modules, which realizes real-time map
construction and online dynamic object removal (DOR). First, a two-layer 3D
region-wise hash map structure of map management is proposed for effective
online DOR. Then, in scan fresher, region-wise ground plane estimation (R-GPE)
is adopted for estimating and preserving ground information and Scan-to-Map
Removal (S2M-R) is proposed to discriminate and remove dynamic regions.
Moreover, the lightweight back-end removal module maintaining keyframes is
proposed for further DOR. As experimentally verified on SemanticKITTI, our
proposed framework yields promising performance on online DOR of map
construction compared with the state-of-the-art methods. And we also validate
the proposed framework in real-world environments
Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot
This paper presents an efficient and safe method to avoid static and dynamic
obstacles based on LiDAR. First, point cloud is used to generate a real-time
local grid map for obstacle detection. Then, obstacles are clustered by DBSCAN
algorithm and enclosed with minimum bounding ellipses (MBEs). In addition, data
association is conducted to match each MBE with the obstacle in the current
frame. Considering MBE as an observation, Kalman filter (KF) is used to
estimate and predict the motion state of the obstacle. In this way, the
trajectory of each obstacle in the forward time domain can be parameterized as
a set of ellipses. Due to the uncertainty of the MBE, the semi-major and
semi-minor axes of the parameterized ellipse are extended to ensure safety. We
extend the traditional Control Barrier Function (CBF) and propose Dynamic
Control Barrier Function (D-CBF). We combine D-CBF with Model Predictive
Control (MPC) to implement safety-critical dynamic obstacle avoidance.
Experiments in simulated and real scenarios are conducted to verify the
effectiveness of our algorithm. The source code is released for the reference
of the community.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 202