27 research outputs found
Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
Modern 3D laser-range scanners have a high data rate, making online
simultaneous localization and mapping (SLAM) computationally challenging.
Recursive state estimation techniques are efficient but commit to a state
estimate immediately after a new scan is made, which may lead to misalignments
of measurements. We present a 3D SLAM approach that allows for refining
alignments during online mapping. Our method is based on efficient local
mapping and a hierarchical optimization back-end. Measurements of a 3D laser
scanner are aggregated in local multiresolution maps by means of surfel-based
registration. The local maps are used in a multi-level graph for allocentric
mapping and localization. In order to incorporate corrections when refining the
alignment, the individual 3D scans in the local map are modeled as a sub-graph
and graph optimization is performed to account for drift and misalignments in
the local maps. Furthermore, in each sub-graph, a continuous-time
representation of the sensor trajectory allows to correct measurements between
scan poses. We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality by an
entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
Perception of Deformable Objects and Compliant Manipulation for Service Robots
Abstract We identified softness in robot control as well as robot perception as key enabling technologies for future service robots. Compliance in motion control compensates for small errors in model acquisition and estimation and enables safe physical interaction with humans. The perception of shape similarities and defor-mations allows a robot to adapt its skills to the object at hand, given a description of the skill that generalizes between different objects. In this paper, we present our approaches to compliant control and object manipulation skill transfer for service robots. We report on evaluation results and public demonstrations of our ap-proaches. 1
A Three-Point Directional Search Block Matching Algorithm
This paper proposes compact directional asymmetric search patterns, which we have named as three-point directional search (TDS). In most fast search motion estimation algorithms, a symmetric search pattern is usually set at the minimum block distortion point at each step of the search. The design of the symmetrical pattern in these algorithms relies primarily on the assumption that the direction of convergence is equally alike in each direction with respect to the search center. Therefore, the monotonic property of real-world video sequences is not properly used by these algorithms. The strategy of TDS is to keep searching for the minimum block distortion point in the most probable directions, unlike the previous fast search motion estimation algorithms where all the directions are checked. Therefore, the proposed method significantly reduces the number of search points for locating a motion vector. Compared to conventional fast algorithms, the proposed method has the fastest search speed and most satisfactory PSNR values for all test sequences
A Survey on Block Matching Algorithms for Video Coding
Block matching algorithm (BMA) for motion estimation (ME) is the heart to many motion-compensated video-coding techniques/standards, such as ISO MPEG-1/2/4 and ITU-T H.261/262/263/264/265, to reduce the temporal redundancy between different frames. During the last three decades, hundreds of fast block matching algorithms have been proposed. The shape and size of search patterns in motion estimation will influence more on the searching speed and quality of performance. This article provides an overview of the famous block matching algorithms and compares their computational complexity and motion prediction quality