Robust Object-based SLAM for High-speed Autonomous Navigation
We present Robust Object-based SLAM for High-speed Autonomous Navigation (ROSHAN), a novel approach to object-level mapping suitable for autonomous navigation. In ROSHAN, we represent objects as ellipsoids and infer their parameters using three sources of information - bounding box detections, image...
Main Authors: | Ok, Kyel, Liu, Katherine Y, Frey, Kristoffer M. (Kristoffer Martin), How, Jonathan P, Roy, Nicholas |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
|
Online Access: | https://hdl.handle.net/1721.1/130015 |
Similar Items
-
Hierarchical Object Map Estimation for Efficient and Robust Navigation
by: Ok, Kyel, et al.
Published: (2022) -
Efficient constellation-based map-merging for semantic SLAM
by: Frey, Kristoffer M., et al.
Published: (2021) -
Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM
by: Frey, Kristoffer M., et al.
Published: (2021) -
Multi-level mapping: Real-time dense monocular SLAM
by: Greene, William N., et al.
Published: (2017) -
Belief-Space Planning for Real-World Systems: Efficient SLAM-Based Belief Propagation and Continuous-Time Safety
by: Frey, Kristoffer M.
Published: (2022)