Sanjiv Singh - IEEE Xplore Author Profile

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Author details

Sanjiv Singh

Publications

3

Citations

183

Publications by Year

20042024

Co-Authors:

Dan AbadVasu AgrawalTim AngertGreg ArmstrongGraeme Best

Show All Co-Authors (53)

Sanjiv Singh

Affiliation

Carnegie Mellon University

Publication Topics

Balance Of System,
Dead Reckoning,
Distance Estimation,
Emergency Response,
Firefighters,
Flashlight,
Global Map,
Ground Plane,
Human Operator,
Inertial Measurement Unit,
Kalman Filter,
Line-of-sight

Biography

Sanjiv Singh is an associate research professor at the Robotics Institute, Carnegie Mellon University. His recent work includes perception in natural and dynamic environments (developing autonomous ground and air vehicles to navigate with little or no a priori information using onboard sensors) and multiagent coordination (developing teams of agents made of humans, embedded sensors, and robots to perform tasks more efficiently and reliably). He received his PhD in robotics from Carnegie Mellon. Contact him at Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA 15213; ssingh+@cmu.edu; www.frc.ri.cmu.edu/~ssinqh.(Based on document published on 17 January 2005).

Publications

3

Citations

183

Publications by Year

20042024

Co-Authors:

Dan Abad
Vasu Agrawal
Tim Angert
Greg Armstrong
Graeme Best

Show All Co-Authors (53)

Author's Published Works

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Dynamic Point Cloud Interpolation

Anique Akhtar;Zhu Li;Geert Van der Auwera;Jianle Chen

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2022 | Conference Paper |

Cited by: Papers (7)

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Dense photorealistic point clouds can depict real-world dynamic objects in high resolution and with a high frame rate. Frame interpolation of such dynamic point clouds would enable the distribution, processing, and compression of such content. In this work, we propose a first point cloud interpolation framework for photorealistic dynamic point clouds. Given two consecutive dynamic point cloud fram...Show More

Dynamic Point Cloud Interpolation

Anique Akhtar;Zhu Li;Geert Van der Auwera;Jianle Chen

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2022 | Conference Paper |

Experimental Assessment of Feature-based Lidar Odometry and Mapping

Asad Ullah Khan;Ernesto Fontana;Dario Lodi Rizzini;Stefano Caselli

2022 Sixth IEEE International Conference on Robotic Computing (IRC)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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This paper experimentally evaluates the performance of Lidar Odometry and Mapping (LOAM) algorithms based on two different features namely edges and planar surfaces. This work substitutes the LOAM current feature extraction method with novel SKIP-3D (SKeleton Interest Point 3D) which exploits the sparse Lidar point clouds obtained from 3D Lidar to extract high curvature points in the scan through ...Show More

Experimental Assessment of Feature-based Lidar Odometry and Mapping

Asad Ullah Khan;Ernesto Fontana;Dario Lodi Rizzini;Stefano Caselli

2022 Sixth IEEE International Conference on Robotic Computing (IRC)

Year: 2022 | Conference Paper |

Circular sequential k-out-of-n congestion system

IEEE Transactions on Reliability

Year: 2005 | Volume: 54, Issue: 3 | Journal Article |

Cited by: Papers (11)

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A circular sequential k-out-of-n congestion (CSknC) model is presented. This model finds use in reliable systems to prevent single-point failure, such as (k,n) secret key sharing systems. The model assumes that each of the n servers has a known congestion probability. These n servers are arranged in a circle, and are connected sequentially. A server is connected successfully if it is not congested...Show More

Circular sequential k-out-of-n congestion system

IEEE Transactions on Reliability

Year: 2005 | Volume: 54, Issue: 3 | Journal Article |

DRI-LOAM: A LOAM-Based Dynamic Objects Removal Strategy Using Range Images

Xiaohua Wang;Yuqian Wang;Jian Liu;Zhenan Tian;Li Li

2023 42nd Chinese Control Conference (CCC)

Year: 2023 | Conference Paper |

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The presence of dynamic points is unavoidable in scan data of urban environments, which creates unwanted traces in the map. These traces of dynamic objects may act as obstacles and thus impede good localization and navigation performances. This paper proposes a novel dynamic points removal strategy. Firstly, a LOAM based algorithm is used to generate a set of local maps. Then alignment is performe...Show More

DRI-LOAM: A LOAM-Based Dynamic Objects Removal Strategy Using Range Images

Xiaohua Wang;Yuqian Wang;Jian Liu;Zhenan Tian;Li Li

2023 42nd Chinese Control Conference (CCC)

Year: 2023 | Conference Paper |

Bi-Directional Inter-Prediction For Geometry-Based Point Cloud Compression

Yingzhan Xu;Kai Zhang;Wenyi Wang;Li Zhang

2022 IEEE International Conference on Image Processing (ICIP)

Year: 2022 | Conference Paper |

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Efficient 3D point cloud compression plays critical role in immersive multimedia presentation and autonomous driving. The temporal redundancy between consecutive point cloud frames is obvious, but the exploration of inter-prediction is insufficient in the geometry-based point cloud compression (G-PCC) framework. In inter exploration model (Inter-EM) of G-PCC, the reference information can only com...Show More

Bi-Directional Inter-Prediction For Geometry-Based Point Cloud Compression

Yingzhan Xu;Kai Zhang;Wenyi Wang;Li Zhang

2022 IEEE International Conference on Image Processing (ICIP)

Year: 2022 | Conference Paper |

Sparse Convolution Based Octree Feature Propagation for Lidar Point Cloud Compression

Muhammad Asad Lodhi;Jiahao Pang;Dong Tian

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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With the advent of new 3D scanning technologies, point clouds have become a crucial way to depict real and virtual objects/scenes. Point clouds represent the continuous sur-faces of underlying object/scene through a collection (usually millions) of discrete, irregular, and often sparsely distributed 3D samples on the surface of the objects, e.g. LiDAR scans. This nature of point cloud data present...Show More

Sparse Convolution Based Octree Feature Propagation for Lidar Point Cloud Compression

Muhammad Asad Lodhi;Jiahao Pang;Dong Tian

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Application of a Depth Camera for Constructing Complex Three-Dimensional Models in Multiple Scanning Complexes

Denis V. Shilin;Svetlana I. Vtyurina;Shuvra Dey;Ilya D. Zabarin

2024 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

Year: 2024 | Conference Paper |

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This work provides a solution to the problem of determining camera displacement in space using 3D data analysis, specifically by searching for reference landmarks that can “stitch together” point clouds, resulting in the construction of complex objects. The solution is achieved geometrically. The reference landmarks are local maxima and convex points identified in different planes of the point clo...Show More

Application of a Depth Camera for Constructing Complex Three-Dimensional Models in Multiple Scanning Complexes

Denis V. Shilin;Svetlana I. Vtyurina;Shuvra Dey;Ilya D. Zabarin

2024 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

Year: 2024 | Conference Paper |

Lidar Odometry and Mapping Optimized by the Theory of Functional Systems in the Parking Lot

J. Yi;M.S. Selezneva;K.A. Neusypin

2022 International Russian Automation Conference (RusAutoCon)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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We propose a lidar odometry and mapping (LOAM) method optimized by the theory of functional system to map the parking lot and estimate the pose of the ground vehicle in real-time, providing sufficient and reliable prior information for subsequent intelligent parking. An important intelligent component in the theory of functional system is action result acceptor. The constructed action result accep...Show More

Lidar Odometry and Mapping Optimized by the Theory of Functional Systems in the Parking Lot

J. Yi;M.S. Selezneva;K.A. Neusypin

2022 International Russian Automation Conference (RusAutoCon)

Year: 2022 | Conference Paper |

Research on Identification Method Based on Point Cloud Sequence with Millimeter-Wave Radar

Jing Qi;Xinyuan Cheng;Shuang Fu

2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP)

Year: 2024 | Conference Paper |

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The point cloud data obtained from millimeter-wave radar has shown strong potential in pedestrian recognition tasks after tracking a target. This paper proposes a new deep learning model that can extract features from the point cloud sequence data of a target. The proposed model solves the problem of low accuracy of existing methods in pedestrian free-walking scenarios. The model's performance in ...Show More

Research on Identification Method Based on Point Cloud Sequence with Millimeter-Wave Radar

Jing Qi;Xinyuan Cheng;Shuang Fu

2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP)

Year: 2024 | Conference Paper |

Quantitative Reliability Assessment for 3D LiDAR Point Cloud Data Frames

Yuki Umemoto;Junya Sato;Yoshiki Yamaguchi;Taku Yamazaki;Takumi Miyoshi;Ryoichi Shinkuma

2025 IEEE International Conference on Consumer Electronics (ICCE)

Year: 2025 | Conference Paper |

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Digital twin technology plays a crucial role in realizing advanced mobility services such as autonomous driving; however, it is at risk because of potential tampering with three-dimensional (3D) point cloud data collected by Light Detection And Ranging (LiDAR) systems. This paper proposes a method to detect tampering in 3D point cloud data based on LiDAR scan line characteristics and to estimate a...Show More

Quantitative Reliability Assessment for 3D LiDAR Point Cloud Data Frames

Yuki Umemoto;Junya Sato;Yoshiki Yamaguchi;Taku Yamazaki;Takumi Miyoshi;Ryoichi Shinkuma

2025 IEEE International Conference on Consumer Electronics (ICCE)

Year: 2025 | Conference Paper |

Sequential Point Cloud Upsampling by Exploiting Multi-Scale Temporal Dependency

Kaisiyuan Wang;Lu Sheng;Shuhang Gu;Dong Xu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2021 | Volume: 31, Issue: 12 | Journal Article |

Cited by: Papers (12)

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In this work, we propose a new sequential point cloud upsampling method called SPU, which aims to upsample sparse, non-uniform, and orderless point cloud sequences by effectively exploiting rich and complementary temporal dependency from multiple inputs. Specifically, these inputs include a set of multi-scale short-term features from the 3D points in three consecutive frames (i.e., the previous/cu...Show More

Sequential Point Cloud Upsampling by Exploiting Multi-Scale Temporal Dependency

Kaisiyuan Wang;Lu Sheng;Shuhang Gu;Dong Xu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2021 | Volume: 31, Issue: 12 | Journal Article |

On-the-Fly Spatio-Temporal Human Segmentation of 3D Point Cloud Data By Micro-Size LiDAR

Yuma Okochi;Hamada Rizk;Hirozumi Yamaguchi

2022 18th International Conference on Intelligent Environments (IE)

Year: 2022 | Conference Paper |

Cited by: Papers (7)

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The technology of 3D recognition is evolving rapidly, enabling unprecedented growth of applications towards human-centric intelligent environments. On top of these applications human segmentation is a key technology towards analyzing and understanding human mobility in those environments. However, existing segmentation techniques rely on deep learning models, which are computationally intensive an...Show More

On-the-Fly Spatio-Temporal Human Segmentation of 3D Point Cloud Data By Micro-Size LiDAR

Yuma Okochi;Hamada Rizk;Hirozumi Yamaguchi

2022 18th International Conference on Intelligent Environments (IE)

Year: 2022 | Conference Paper |

Graph Filter-Based Fast Motion Matching for Inter Frame Coding of MPEG G-PCC

Tian Guo;Hui Yuan;Tingting Wang;Wei Gao

2022 IEEE International Conference on Image Processing (ICIP)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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As a key technique for geometry-based point cloud inter frame coding, motion estimation (ME) has shown the effectiveness to improve the coding efficiency. But the high complexity limits its application. We propose a novel and effective method for fast global motion matching between consecutive frames. We first divide an input point cloud into several blocks, and construct a graph for each block. T...Show More

Graph Filter-Based Fast Motion Matching for Inter Frame Coding of MPEG G-PCC

Tian Guo;Hui Yuan;Tingting Wang;Wei Gao

2022 IEEE International Conference on Image Processing (ICIP)

Year: 2022 | Conference Paper |

3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints

Kojiro Takeyama

2020 Digital Image Computing: Techniques and Applications (DICTA)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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This paper presents a novel edge-based 3D reconstruction method using a monocular camera. The edge information is known to be illumination-invariant and to include abundant structural information in a relatively small number of pixels. However, since edge line cannot explicitly determine the pixel-to-pixel correspondence as in the feature point approach, it is difficult to perform accurate matchin...Show More

3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints

Kojiro Takeyama

2020 Digital Image Computing: Techniques and Applications (DICTA)

Year: 2020 | Conference Paper |

Sequential Semantic Descriptor from 3D Point Clouds for Place Recognition

Chujia Lin;Yiqi Liu;An Chen;Hongxia Gao

2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI)

Year: 2024 | Conference Paper |

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The closed-loop detection of 3D point clouds remains a significant challenge due to the complexities involved in generating effective descriptors that are robust to occlusion and viewpoint changes. Unlike most existing methods focusing on extracting local, global, and statistical features from the raw point clouds of a single frame, our approach emphasizes the utilization of semantic-level scene g...Show More

Sequential Semantic Descriptor from 3D Point Clouds for Place Recognition

Chujia Lin;Yiqi Liu;An Chen;Hongxia Gao

2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI)

Year: 2024 | Conference Paper |

Learning Spatial-Temporal Embeddings for Sequential Point Cloud Frame Interpolation

Lili Zhao;Zhuoqun Sun;Lancao Ren;Qian Yin;Lei Yang;Meng Guo

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

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A point cloud sequence is usually acquired at a low frame rate owing to the limitations from the sensing equipment. Consequently, the immersive experience of the virtual reality might be greatly degraded. To tackle this issue, a point cloud frame interpolation process can be used to increase the frame rate of the acquired point cloud sequence by generating new frames between the consecutive ones. ...Show More

Learning Spatial-Temporal Embeddings for Sequential Point Cloud Frame Interpolation

Lili Zhao;Zhuoqun Sun;Lancao Ren;Qian Yin;Lei Yang;Meng Guo

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

VPU: A Video-Based Point Cloud Upsampling Framework

Kaisiyuan Wang;Lu Sheng;Shuhang Gu;Dong Xu

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

Cited by: Papers (3)

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In this work, we propose a new patch-based framework called VPU for the video-based point cloud upsampling task by effectively exploiting temporal dependency among multiple consecutive point cloud frames, in which each frame consists of a set of unordered, sparse and irregular 3D points. Rather than adopting the sophisticated motion estimation strategy in video analysis, we propose a new spatio-te...Show More

VPU: A Video-Based Point Cloud Upsampling Framework

Kaisiyuan Wang;Lu Sheng;Shuhang Gu;Dong Xu

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

Adaptive Fractional Open Circuit Voltage Method for Maximum Power Point Tracking in a Photovoltaic Panel

Shubham Negi;Ashis Maity;Amit Patra;Mrigank Sharad

2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID)

Year: 2019 | Conference Paper |

Cited by: Papers (5)

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The output power of a Photovoltaic (PV) panel changes with solar insolation and temperature. Also the P-V (Power-Voltage) and I-V (Current-Voltage) characteristics of a PV cell is highly non-linear. Hence the maximum output power from the PV panel is achieved at a particular voltage (VMPP) and current (IMPP). To improve the overall efficiency, it is important to keep the PV panel to work at Maximu...Show More

Adaptive Fractional Open Circuit Voltage Method for Maximum Power Point Tracking in a Photovoltaic Panel

Shubham Negi;Ashis Maity;Amit Patra;Mrigank Sharad

2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID)

Year: 2019 | Conference Paper |

Design of a Backstepping-Controlled Boost Converter for MPPT in PV Chains

Okba Boutebba;Samia Semcheddine;Fateh Krim;Billel Talbi

2019 International Conference on Advanced Electrical Engineering (ICAEE)

Year: 2019 | Conference Paper |

Cited by: Papers (5)

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The objective of this work is to integrate the backstepping control for tracking the maximum power point of a photovoltaic (PV) chain. This control strategy is applied for a parallel DC-DC converter (type: boost) in order to regulate the output voltage of the PV generator, according to the reference voltage generated by the known perturb and observe (P&O) MPPT (maximum power point tracking) algori...Show More

Design of a Backstepping-Controlled Boost Converter for MPPT in PV Chains

Okba Boutebba;Samia Semcheddine;Fateh Krim;Billel Talbi

2019 International Conference on Advanced Electrical Engineering (ICAEE)

Year: 2019 | Conference Paper |

A Modified ICP Algorithm Based on FAST and Optical Flow for 3D Registration

Konrad Koniarski;Andrzej Myśliński

2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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This paper presents a modified Iterative Closest Point (ICP) algorithm based on a suitable selection of initial points and local optical flow to speed up registration of static scenes with high accuracy. The biggest disadvantages of using standard ICP algorithm are appropriate initialization and effective matching point step in each iteration. In the proposed modification we deal with these proble...Show More

A Modified ICP Algorithm Based on FAST and Optical Flow for 3D Registration

Konrad Koniarski;Andrzej Myśliński

2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS)

Year: 2022 | Conference Paper |

Sequential Point Clouds: A Survey

Haiyan Wang;Yingli Tian

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 8 | Journal Article |

Cited by: Papers (1)

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Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent researc...Show More

Sequential Point Clouds: A Survey

Haiyan Wang;Yingli Tian

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 8 | Journal Article |

Enhancing ADAS Safety: Pedestrian Motion Classification with mmWave Radar Point Cloud

Asma Omri;Ferial Sbiai;Sofiane Sayahi;Hichem Besbes

2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS)

Year: 2024 | Conference Paper |

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Early and precise pedestrian behavior recognition remains a challenge for Advanced Driver-Assistance Systems (ADAS) safety. This paper evaluates two classification methods for pedestrian motion using low-resolution 4D mmWave radar in real-world urban environments. We collected a data set of radar point cloud that captures four different types of pedestrian behavior on the road: “Walk”, “cross”, “h...Show More

Enhancing ADAS Safety: Pedestrian Motion Classification with mmWave Radar Point Cloud

Asma Omri;Ferial Sbiai;Sofiane Sayahi;Hichem Besbes

2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS)

Year: 2024 | Conference Paper |

Spatial-Temporal Consistency Refinement Network for Dynamic Point Cloud Frame Interpolation

Lancao Ren;Lili Zhao;Zhuoqun Sun;Zhipeng Zhang;Jianwen Chen

2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

Year: 2023 | Conference Paper |

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Point cloud frame interpolation aims to improve the frame rate of a point cloud sequence by synthesising intermediate frames between consecutive frames. Most of the existing works only use the scene flow or features, not fully exploring their local geometry context or temporal correlation, which results in inaccurate local structural details or motion estimation. In this paper, we organically comb...Show More

Spatial-Temporal Consistency Refinement Network for Dynamic Point Cloud Frame Interpolation

Lancao Ren;Lili Zhao;Zhuoqun Sun;Zhipeng Zhang;Jianwen Chen

2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

Year: 2023 | Conference Paper |

Algorithm and Application of 3D Reconstruction of Deformed Pipeline Based on Point Cloud Data

Laizhen Wang;Kekuan Wang;Ruibin Duan;Yazhang He;Yinshan Ding

2023 11th International Conference on Information Technology: IoT and Smart City (ITIoTSC)

Year: 2023 | Conference Paper |

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In response to the problem of three-dimensional reconstruction of submarine deformed pipelines in reverse modeling engineering, an algorithm is proposed to directly extract the characteristic parameters of submarine deformed pipelines from scattered measurement data. The first step of the algorithm is to extract feature value data from seabed deformed pipeline data, and use the feature values to f...Show More

Algorithm and Application of 3D Reconstruction of Deformed Pipeline Based on Point Cloud Data

Laizhen Wang;Kekuan Wang;Ruibin Duan;Yazhang He;Yinshan Ding

2023 11th International Conference on Information Technology: IoT and Smart City (ITIoTSC)

Year: 2023 | Conference Paper |

Multi-body ICP: Motion segmentation of rigid objects on dense point clouds

Youngji Kim;Hwasup Lim;Sang Chul Ahn

2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

Year: 2015 | Conference Paper |

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Motion is one of the crucial keys for segmenting moving objects in dynamic environment. This paper proposes an approach of segmenting moving parts in the sequence of point clouds. Different rigid motions in dense point clouds are found and clustered by applying segmentation schemes such as graph-cuts into Iterative Closest Point (ICP) algorithm with initial segmentation from Generalized PCA (GPCA)...Show More

Multi-body ICP: Motion segmentation of rigid objects on dense point clouds

Youngji Kim;Hwasup Lim;Sang Chul Ahn

2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

Year: 2015 | Conference Paper |

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