LiDAR | BestHub

Zhengtong Technical Team

Apr 15, 2025 · Artificial Intelligence

FAST‑LIO2 LiDAR‑IMU SLAM Implementation and Evaluation on Qiji Autonomous Patrol Vehicles

This article presents the technical background of SLAM, explains why GNSS‑based navigation fails in complex urban environments, describes the selection and testing of several LiDAR‑IMU SLAM algorithms—including FAST‑LIO2—on Qiji unmanned vehicles, and details the hardware configuration, algorithmic improvements, experimental workflow, and positioning results achieved in a real‑world patrol project.

FAST-LIO2LiDARRobotics

0 likes · 9 min read

FAST‑LIO2 LiDAR‑IMU SLAM Implementation and Evaluation on Qiji Autonomous Patrol Vehicles

JD Retail Technology

Mar 15, 2021 · Artificial Intelligence

Two JD Retail Papers Accepted at ICRA 2021: Long‑tailed Facial Expression Recognition and Vanishing‑Point‑Aided LiDAR‑Visual‑Inertial Estimator

JD Retail’s Technology and Data Center announced that two of its papers were accepted at ICRA 2021: one presenting a deep balanced learning approach for long‑tailed facial expression recognition, and the other introducing a vanishing‑point‑aided LiDAR‑visual‑inertial estimator for robust state estimation.

LiDARartificial intelligencefacial expression recognition

0 likes · 5 min read

Two JD Retail Papers Accepted at ICRA 2021: Long‑tailed Facial Expression Recognition and Vanishing‑Point‑Aided LiDAR‑Visual‑Inertial Estimator

Amap Tech

Mar 12, 2021 · Fundamentals

MTA Problem in High‑Precision LiDAR Data and Its Correction Algorithms

The article describes how high‑frequency LiDAR scanners on precision mapping vehicles suffer from Multi‑Time‑Around (MTA) errors—mis‑assigning distant returns to near ranges—and explains both internal laser strategies (continuity assumption and variable‑period emission) and a four‑step neighborhood‑weighting algorithm that reliably corrects these artifacts, restoring accurate point‑clouds for automated map generation.

Data ProcessingLiDARMTA

0 likes · 12 min read

MTA Problem in High‑Precision LiDAR Data and Its Correction Algorithms

DataFunTalk

Feb 20, 2020 · Artificial Intelligence

Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations

This article reviews perception technologies used in autonomous heavy‑truck systems—including lane‑line detection, obstacle detection, and LiDAR sensing—detailing traditional and deep‑learning approaches, practical challenges on high‑speed highways, and the cost, performance, and reliability issues faced when moving these solutions to mass production.

LiDARautonomous drivingdeep learning

0 likes · 16 min read

Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations

DataFunTalk

Dec 3, 2019 · Artificial Intelligence

Hardware Technology Challenges and Solutions for Autonomous Driving

This article reviews the evolution of autonomous‑driving hardware, discusses key sensor technologies such as LiDAR and GNSS/IMU, outlines mechanical and electronic challenges—including size, weight, temperature, vibration, and electromagnetic interference—and presents Pony.ai’s PonyAlpha platform as a practical solution.

GNSSHardwareLiDAR

0 likes · 10 min read

Hardware Technology Challenges and Solutions for Autonomous Driving

DataFunTalk

Nov 27, 2019 · Artificial Intelligence

Front‑Fusion Based Recognition Pipeline for High‑Precision Map Static Obstacle Detection

This article presents a comprehensive front‑fusion recognition pipeline for high‑definition map static obstacle detection, detailing depth‑aware mapping, precise multi‑sensor calibration, point‑cloud registration, and semi‑supervised learning techniques that improve detection accuracy over traditional image‑only methods.

AIHD mapLiDAR

0 likes · 11 min read

Front‑Fusion Based Recognition Pipeline for High‑Precision Map Static Obstacle Detection

DataFunTalk

Aug 12, 2019 · Artificial Intelligence

Multi‑Sensor Fusion in Autonomous Driving: Challenges, Prerequisites, and Methods

Pony.ai shares its extensive experience on multi‑sensor perception for autonomous trucks, explaining why sensor fusion is needed, the essential motion‑compensation and calibration steps, and practical camera‑lidar and radar‑lidar fusion techniques that improve detection range and robustness.

CameraLiDARautonomous driving

0 likes · 15 min read

Multi‑Sensor Fusion in Autonomous Driving: Challenges, Prerequisites, and Methods

DataFunTalk

May 8, 2019 · Artificial Intelligence

Perception System Overview: Sensors, Fusion, Onboard Architecture, and Technical Challenges in Autonomous Driving

This article presents a comprehensive overview of autonomous driving perception, covering system fundamentals, sensor setups and fusion techniques, onboard processing architecture, and the key technical challenges such as precision‑recall balance, adverse weather, and small‑object detection.

LiDARautonomous drivingcomputer vision

0 likes · 12 min read

Perception System Overview: Sensors, Fusion, Onboard Architecture, and Technical Challenges in Autonomous Driving

Tencent Cloud Developer

Mar 28, 2018 · Operations

Microsoft Releases Open-Source Tool for Custom Linux Distributions on WSL

Microsoft has launched an open‑source tool that lets distribution maintainers and developers create custom Linux packages for the Windows Subsystem for Linux, while also previewing new WSL features such as background tasks and Unix‑domain sockets, and reporting that Uber disabled safety radar on a Volvo autonomous vehicle involved in a fatal pedestrian crash.

LiDARLinux distributionsMicrosoft

0 likes · 5 min read

Microsoft Releases Open-Source Tool for Custom Linux Distributions on WSL