Table 1 - uploaded by Akira Taniguchi
Content may be subject to copyright.
Source publication
This paper describes a new method for estimating the body shape of a mobile robot by using sensory-motor information. In many biological systems, it is important to be able to estimate body shapes to allow it to appropriately behave in a complex environment. Humans and other animals can form their body image and determine actions based on their rec...
Context in source publication
Context 1
... results of the quantitative evaluation of body shape estimation are shown in Table 1. By treating the body shape estimation problem as an information retrieval task that attempts to estimate whether or not each cell contains a body part, the precision, recall, and F-measure were calculated. ...
Similar publications
This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integrates height priors over the detected objects belonging to a set of broad predefined classes, based on recent advances in fast generic object detection. Each observation is...
Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM). Recently, the self-supervised learning framework that jointly optimizes the relative pose and target image depth has attracted the attention of the community. Previous works rely on the photometric error generated from depth...
This work proposes a new, online algorithm for estimating the local scale correction to apply to the output of a monocular SLAM system and obtain an as faithful as possible metric reconstruction of the 3D map and of the camera trajectory. Within a Bayesian framework, it integrates observations from a deep-learning based generic object detector and...
We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refin...
We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both indoor/outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surpass the state-of-the-art for mon...
Citations
... The process of estimating the position of the robot involves identifying the location with the highest probability of being the robot's position, based on sensor data analysed through maximum a posteriori (MAP) estimation. The two methods most commonly used Taniguchi et al. (2017). in the SLAM back-end stage are EKF SLAM and FastSLAM. EKF SLAM utilizes an extended Kalman filter algorithm and treats the state of the robot and map as a Gaussian model. ...
We introduce a factorization method to increase the calculation speed of incremental smoothing and mapping using Bayes tree (iSAM2), which is used in the back-end stage of simultaneous localization and mapping (SLAM), and to analyse the cause of the associated estimation error. iSAM2 is the method most commonly used to increase the accuracy of SLAM and shorten the calculation time required in real dense situations. In this paper, we describe the application of CUR matrix decomposition to iSAM2’s sparse linear system solver. CUR matrix decomposition is one of the low-rank matrix decomposition methods. It consists of matrices C and R, which are sets of columns and rows of the original matrix, and matrix U, which approximates the original matrix. Because of the characteristics of CUR matrix decomposition, it is possible to effectively approximate the sparse information matrix. Also, using principal component analysis, it is possible to identify the factors that increase or decrease the estimation error. We confirmed the feasibility of the proposed analysis method by applying it to real datasets and obtaining estimation errors similar to those obtained with iSAM2.
Robotics is the multi-disciplinary domain that is booming in today’s world, and expanding its roots deep into various fields of research, manufacturing industries, healthcare, and even in our day-to-day lives. Nevertheless, as with any other evolving technology, robotics face numerous challenges. In this context, lately, blockchain technology has been identified as a promising technology to resolve many of these issues such as identification of malicious/rogue nodes, malfunctioning/faults in automated processes, non-compliance to the agreed norms and privacy rules, security attacks on robotic systems, and non-transparency in performance monitoring and audits. In particular, blockchain with its features like decentrality, immutability, provenance, low operational cost, tight access control, and trustworthy operations, can offer significant improvements to new applications and use cases driven by robotics. Thus, the paper begins with exploring the key requirements and technical challenges encountered by robots in general. Next, it provides detailed overview of blockchain technology in a tutorial style. Then, the role of blockchain for different uses cases of robotics are surveyed. Furthermore, various technical challenges that need to be mitigated to harness full potential of blockchain for robotics are highlighted. Finally, the future research directions are presented that can pave the way ahead for advancements and profitable integration of blockchain in the realm of robotics.
