6 papers
Improving Map Consistency in Graph-Based LiDAR SLAM Through Information-Aware Odometry and Retroactive Loop Closure
Saurabh Gupta, Niklas Trekel, Louis Wiesmann +1
High-quality maps are fundamental for robotics tasks such as navigation and planning. Although modern graph-based LiDAR SLAM systems achieve good trajectory accuracies, a low traje…
PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map
Yue Pan, Xingguang Zhong, Liren Jin +4
Robots benefit from high-fidelity reconstructions of their environment, which should be geometrically accurate and photorealistic to support downstream tasks. While this can be ach…
Improving Indoor Localization Accuracy by Using an Efficient Implicit Neural Map Representation
Haofei Kuang, Yue Pan, Xingguang Zhong +3
Globally localizing a mobile robot in a known map is often a foundation for enabling robots to navigate and operate autonomously. In indoor environments, traditional Monte Carlo lo…
Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories
Louis Wiesmann, Elias Marks, Saurabh Gupta +3
Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from…
PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
Yue Pan, Xingguang Zhong, Louis Wiesmann +3
Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, cal…
LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters
Yibin Wu, Tiziano Guadagnino, Louis Wiesmann +3
Odometry estimation is crucial for every autonomous system requiring navigation in an unknown environment. In modern mobile robots, 3D LiDAR-inertial systems are often used for thi…