Benchmarking and Comparing Popular Visual SLAM Algorithms

This paper contains the performance analysis and benchmarking of two popular visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The dataset selected has a large set of image sequences from a Microsoft Kinect RGB-D sensor with highly accurate and time-synchronized ground truth poses from a motion capture system. The test sequences selected depict a variety of problems and camera motions faced by Simultaneous Localization and Mapping (SLAM) algorithms for the purpose of testing the robustness of the algorithms in different situations. The evaluation metrics used for the comparison are Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis involves comparing the Root Mean Square Error (RMSE) of the two metrics and the processing time for each algorithm. This paper serves as an important aid in the selection of SLAM algorithm for different scenes and camera motions. The analysis helps to realize the limitations of both SLAM methods. This paper also points out some underlying flaws in the used evaluation metrics.Comment: 7 pages, 4 figure

ISMA-DS/CDMA MAC protocol for mobile packet radio networks

In this paper an ISMA-DS/CDMA MAC protocol for a packet transmission network is presented. The main feature of this protocol is its ability to retain the inherent flexibility of random access protocols while at the same time reducing to some extent the randomness in the access in order to increase the system capacity. In this framework, the protocol is adapted to a frame structure similar to that specified in the UTRA ETSI proposal for third generation mobile communication systems. Additionally, some adaptive mechanisms are proposed that improve protocol performance by means of varying the transmission bit rate according to the channel load that is broadcast by the base station. As a result, an adaptive bit rate algorithm is presented that reaches a throughput value close to the optimumPeer ReviewedPostprint (published version

A bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of non-rigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated. An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current non-rigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.Peer ReviewedPostprint (author's final draft

Efficient Grover search with Rydberg blockade

We present efficient methods to implement the quantum computing Grover search algorithm using the Rydberg blockade interaction. We show that simple pi-pulse excitation sequences between ground and Rydberg excited states readily produce the key conditional phase shift and inversion-about-the mean unitary operations for the Grover search. Multi-qubit implementation schemes suitable for different properties of the atomic interactions are identifed and the error scaling of the protocols with system size is found to be promising for immediate experimental investigation.Comment: Detailed description of algorithm for sub-register architecture. Error budget modified for Cs atomic parameters. To appear in J. Phys. B. Special Issue on Strong Rydberg interactions in ultracold atomic and molecular gase

Volume-based Semantic Labeling with Signed Distance Functions

Research works on the two topics of Semantic Segmentation and SLAM (Simultaneous Localization and Mapping) have been following separate tracks. Here, we link them quite tightly by delineating a category label fusion technique that allows for embedding semantic information into the dense map created by a volume-based SLAM algorithm such as KinectFusion. Accordingly, our approach is the first to provide a semantically labeled dense reconstruction of the environment from a stream of RGB-D images. We validate our proposal using a publicly available semantically annotated RGB-D dataset and a) employing ground truth labels, b) corrupting such annotations with synthetic noise, c) deploying a state of the art semantic segmentation algorithm based on Convolutional Neural Networks.Comment: Submitted to PSIVT201