This excellent book changes the game for autonomous robot navigation. By presenting and documenting efficient robot navigation topics and methods in a novel skills-related way, Tsintotas et al. show readers how to deliver high performance that satisfies both industry expectations and standards.

Robot navigation topics are projected under runtime and memory-constrained approaches. Readers can improve their knowledge and abilities, and then objectively evaluate autonomous robot navigation performance relative to industry expectations. Indeed, the discourse revolves around looking at navigation topics through the prisms of runtime and memory constraints, offering a profoundly sagacious approach. The book includes a plethora of online appearance-based place recognition and mapping pipelines, each meticulously crafted based on three distinct mapping techniques that address loop-closure detection in the context of mobile platforms possessing finite computational resources. The authors thoughtfully provide open-source coding, generously made available to educational and research communities, as a litmus test for gauging performance in academic circles.

The cherry on top is the book’s vibrant and captivating full-color presentation. Moreover, it deftly appraises the latest state-of-the-art innovations and advancements in place recognition and mapping, cementing its position as an indispensable reference for the robot navigation industry.