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A Multisensor Dataset for SLAM in Repetitive Scene Environments
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AI
DOI：10.1109/LSENS.2026.3667691.png)
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Abstract
En 中文
Accurate simultaneous localization and mapping (SLAM) is fundamental to autonomous navigation, as it requires both estimating the robot's motion and consistently constructing or aligning a map. However, most existing datasets are collected in feature-rich environments and do not adequately address repetitive scenes that cause perceptual aliasing, drift in odometry, and failures in loop closure. We present a new multisensor dataset specifically designed to evaluate SLAM performance in repetitive scene environments. It includes two representative scenarios: a riverside bikepath that exhibits frame-level repetition and an urban development district that presents block-level repetition. Additional campus sequences are provided as nonrepetitive baselines. The platform integrates two 3-D LiDARs, an red-green-blue-depth (RGB-D) camera, three IMUs, and GNSS. By explicitly incorporating both frame-level and block-level repetitive patterns, this dataset enables systematic analysis of perceptual aliasing in SLAM and serves as a reproducible benchmark for developing robust SLAM systems.
Keywords:
Simultaneous localization and mapping
Laser radar
Global navigation satellite system
Trajectory
Roads
Odometry
Location awareness
Calibration
Cameras
Robot sensing systems
Sensor systems
localization
mapping
navigation
robotics
SLAM
Journal
I
IF:
2.2
Papers:
354
Citations:
3.1K