Part 3: Simultaneous Localization & Mapping: Which SLAM Is For You? on OpenCV Live!

2026-02-02 · Source: OpenCV · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

OpenCV is hosting a live webinar on Simultaneous Localization & Mapping (SLAM), featuring Ali Pahlevani of SLAMbotics, as the final installment of a three-part series. This one-hour event, scheduled for Thursday, February 5th, at 9:00 AM Pacific, will delve into critical considerations for SLAM implementation, including the efficacy of purely visual SLAM versus systems incorporating LiDAR, and distinctions between indoor and outdoor use cases. Attendees can participate in a live Q&A session and a trivia segment for a chance to win prizes. The webinar will be accessible across multiple platforms, including YouTube, Twitch, Zoom, and LinkedIn, with previous parts available on YouTube.

Key takeaway

For Computer Vision Engineers evaluating SLAM solutions, attending this webinar will clarify the trade-offs between visual-only and LiDAR-assisted SLAM, and how environmental factors like indoor versus outdoor settings influence system design. Your participation in the live Q&A can directly address specific implementation challenges you face, helping you refine your approach to robotics and autonomous navigation projects.

Key insights

The webinar explores key considerations and challenges in Simultaneous Localization & Mapping (SLAM) applications.

Principles

• SLAM is a highly active field
• Visual SLAM has specific use cases

In practice

• Attend live Q&A sessions
• Submit project for feature consideration

Topics

• Simultaneous Localization & Mapping
• Visual SLAM
• LiDAR SLAM
• Indoor Robotics
• Outdoor Robotics

Best for: Computer Vision Engineer, Robotics Engineer, AI Engineer

Related on AIssential

• DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild — DSP-SLAM++ unifies real-time, multi-class, high-fidelity object SLAM using an asynchronous pipeline and fisheye-LiDAR fusion.
• SceneAligner: 3D-Grounded Floorplan Localization in the Wild — SceneAligner enables robust floorplan localization in complex, real-world settings by grounding the task in 3D scene reconstruction.
• Effective and Low-cost Lane-based Map Localization for Vehicle-Centric Route Generation — OLRA aligns map routes with camera-detected lanes for enhanced localization and consistent driver-view route generation, outperforming direct methods.
• Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots — Hardware sensor choices critically determine multimodal SLAM resilience and accuracy on agile quadruped robots.
• See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View — Isolating target-visible navigation and using dynamic 3D cues improves UAV precision reaching.
• Real-Time Face Tracking: OpenCV Control of a UR Robot — Real-time face tracking can provide intuitive, continuous control for robotic manipulators.

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Editorial summary, takeaway, and curation by AIssential. Original article published by OpenCV.