Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map

By Giseop Kim, Ayoung Kim. IEEE/RJS International Conference on Intelligent RObots and Systems, 2018

912 citations recorded by the source corpus.

Compared to diverse feature detectors and descriptors used for visual scenes, describing a place using structural information is relatively less reported. Recent advances in simultaneous localization and mapping (SLAM) provides dense 3D maps of the environment and the localization is proposed by diverse sensors. Toward the global localization based on the structural information, we propose Scan Context, a non-histogram-based global descriptor from 3D Light Detection and Ranging (LiDAR) scans. Unlike previously reported methods, the proposed approach directly records a 3D structure of a visible space from a sensor and does not rely on a histogram or on prior training. In addition, this approach proposes the use of a similarity score to calculate the distance between two scan contexts and also a two-phase search algorithm to efficiently detect a loop. Scan context and its search algorithm make loop-detection invariant to LiDAR viewpoint changes so that loops can be detected in places such as reverse revisit and corner. Scan context performance has been evaluated via various benchmark datasets of 3D LiDAR scans, and the proposed method shows a sufficiently improved performance.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/007673fe281e58fbbdca687c4d7d72dfe2240451

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Chen Wang
2. Lihua Xie
3. Han Wang
4. Sunwook Choi
5. Giseop Kim
6. Ayoung Kim
7. Daniela Rus
8. Carlo Ratti

In Computer Vision and Pattern Recognition

1. Chen Wang
2. Lihua Xie
3. Han Wang
4. Sunwook Choi
5. Ayoung Kim
6. Giseop Kim
7. Daniela Rus
8. Carlo Ratti

In Geology

1. Sunwook Choi
2. Chen Wang
3. Lihua Xie
4. Han Wang
5. Giseop Kim
6. Ayoung Kim
7. Younghun Cho
8. Yeong-Sang Park

Citations in the corpus

• Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection (2020, methodology): “Inspired by scan context [21], [22] and shape context [23], we introduce intensity scan context that can efficiently integrate both geometry and intensity characteristics into a global signature.”
• LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “We also note that our proposed framework is compatible with other methods for loop closure detection, for example, [22] and [23], which generate a point cloud descriptor and use it for place recognition.”
• MulRan: Multimodal Range Dataset for Urban Place Recognition (2020, methodology): “dated that our previous location descriptor and its search algorithm [2] are highly effective for radar place recognition method.”
• Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments (2021, methodology): “Recognizing a previously visited place is important for various robot missions (e.g., loop detection in simultaneous localization and mapping (SLAM) [1], global localization for a kidnapped robot [2], or multi-robot mapping [3]).”