activity
20192022
most citedsbp-env: Sampling-based Motion Planners' Testing Environment

6 citations · 20 across the 7 of their papers we have counts for

collaborators

11 papers

cs.RO20221 cited

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding

Tin Lai

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while…

cs.RO20216 cited

sbp-env: Sampling-based Motion Planners' Testing Environment

Tin Lai

Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses o…

cs.RO20214 cited

Parallelised Diffeomorphic Sampling-based Motion Planning

Tin Lai, Weiming Zhi, Tucker Hermans +1

We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphi…

cs.LG20211 cited

Learning ODEs via Diffeomorphisms for Fast and Robust Integration

Weiming Zhi, Tin Lai, Lionel Ott +2

Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs). In the context of machine lear…

cs.RO20214 cited

Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planning

Tin Lai

Sampling-based motion planners perform exceptionally well in robotic applications that operate in high-dimensional space. However, most works often constrain the planning workspace…

cs.RO2020

Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements

Weiming Zhi, Tin Lai, Lionel Ott +1

Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This pape…